Статті в журналах: "Selective classifier"

1

Pernkopf, Franz. "Bayesian network classifiers versus selective -NN classifier." Pattern Recognition 38, no. 1 (January 2005): 1–10. http://dx.doi.org/10.1016/j.patcog.2004.05.012.

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2

Wares, Scott, John Isaacs, and Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting." Journal of Information & Knowledge Management 20, no. 02 (April 23, 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.

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Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.

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3

Li, Kai, and Hong Tao Gao. "A Subgraph-Based Selective Classifier Ensemble Algorithm." Advanced Materials Research 219-220 (March 2011): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.261.

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To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented. Firstly, a set of classifiers are generated by bootstrap sampling technique and support vector machine learning algorithm. And a complete undirected graph is constructed whose vertex is classifier and weight of edge between a pair of classifiers is diversity values. Secondly, by searching technique to find an edge with minimum weight and to calculate similarity values about two vertexes which is related to the edge, vertex with smaller similarity value is removed. According to this method, a subgraph is obtained. Finally, we choose vertexes of subgraph, i.e. classifiers, as ensemble members. Experiments show that presented method outperforms the traditional ensemble learning methods in classification accuracy.

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4

Wiener, Yair, and Ran El-Yaniv. "Agnostic Pointwise-Competitive Selective Classification." Journal of Artificial Intelligence Research 52 (January 26, 2015): 171–201. http://dx.doi.org/10.1613/jair.4439.

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Pointwise-competitive classifier from class F is required to classify identically to the best classifier in hindsight from F. For noisy, agnostic settings we present a strategy for learning pointwise-competitive classifiers from a finite training sample provided that the classifier can abstain from prediction at a certain region of its choice. For some interesting hypothesis classes and families of distributions, the measure of this rejected region is shown to be diminishing at a fast rate, with high probability. Exact implementation of the proposed learning strategy is dependent on an ERM oracle that can be hard to compute in the agnostic case. We thus consider a heuristic approximation procedure that is based on SVMs, and show empirically that this algorithm consistently outperforms a traditional rejection mechanism based on distance from decision boundary.

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5

Wang, Yan, Xiu Xia Wang, and Sheng Lai. "A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 55–58. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.55.

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In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.

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6

Liu, Li Min, and Xiao Ping Fan. "A Survey: Clustering Ensemble Selection." Advanced Materials Research 403-408 (November 2011): 2760–63. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2760.

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Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.

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7

Nikhar, Sonam, and A. M. Karandikar. "Prediction of Heart Disease Using Different Classification Techniques." APTIKOM Journal on Computer Science and Information Technologies 2, no. 2 (July 1, 2017): 68–76. http://dx.doi.org/10.11591/aptikom.j.csit.106.

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Data mining is one of the essential areas of research that is more popular in health organization. Heart disease is the leading cause of death in the world over the past 10 years. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. This research intends to provide a detailed description of Naïve Bayes, decision tree classifier and Selective Bayesian classifier that are applied in our research particularly in the prediction of Heart Disease. It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on Cleveland datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on this dataset of which C4.5 outperform NB. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classifier and experiment also reveals that selective Bayesian classifier has a better accuracy as compared to other classifiers.

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8

Tao, Xiaoling, Yong Wang, Yi Wei, and Ye Long. "Network Traffic Classification Based on Multi-Classifier Selective Ensemble." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 8, no. 2 (September 9, 2015): 88–94. http://dx.doi.org/10.2174/235209650802150909112547.

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9

Wei, Leyi, Shixiang Wan, Jiasheng Guo, and Kelvin KL Wong. "A novel hierarchical selective ensemble classifier with bioinformatics application." Artificial Intelligence in Medicine 83 (November 2017): 82–90. http://dx.doi.org/10.1016/j.artmed.2017.02.005.

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10

Zhang, Xiao Hua, Zhi Fei Liu, Ya Jun Guo, and Li Qiang Zhao. "Selective Facial Expression Recognition Using fastICA." Advanced Materials Research 433-440 (January 2012): 2755–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2755.

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This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.

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11

Lin, Hung-Chun, and Chao-Ton Su. "A selective Bayes classifier with meta-heuristics for incomplete data." Neurocomputing 106 (April 2013): 95–102. http://dx.doi.org/10.1016/j.neucom.2012.10.020.

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12

M.sabbar, Bayan, and Hussein A. Rasool. "AUTOMATIC MODULATION CLASSIFIER: REVIEW." Iraqi Journal of Information & Communications Technology 3, no. 4 (December 31, 2020): 11–32. http://dx.doi.org/10.31987/ijict.3.4.111.

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The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations with multi-path fading, frequency-selective in addition to the time-varying channels. There are 2 methods could be utilized to decide the classification signal technique: Feature-based (FB) approach and the Maximum likelihood functions (LB) method. With regard to the FB (referred to as pattern-recognition) classification method used in the study. In the presented work, thorough study is provided to find easy method to identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statistic features, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determine and discriminate modulation types have been examined. The number of the classifiers which have been utilized in the process of discrimination have been studied thoroughly and compared for helping researchers in determining and finding the drawbacks with pattern-recognition according to past works. The presented study serving as guide with regard to studies of AMC for determining adequate algorithms and features.

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13

Chaganti, Rajasekhar, Furqan Rustam, Isabel De La Torre Díez, Juan Luis Vidal Mazón, Carmen Lili Rodríguez, and Imran Ashraf. "Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques." Cancers 14, no. 16 (August 13, 2022): 3914. http://dx.doi.org/10.3390/cancers14163914.

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Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.

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14

Lim, Hyunjun, Byeongnam Kim, Gyu-Jeong Noh, and Sun Yoo. "A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal." Sensors 19, no. 2 (January 18, 2019): 384. http://dx.doi.org/10.3390/s19020384.

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Side effects occur when excessive or low doses of analgesics are administered compared to the required amount to mediate the pain induced during surgery. It is important to accurately assess the pain level of the patient during surgery. We proposed a pain classifier based on a deep belief network (DBN) using photoplethysmography (PPG). Our DBN learned about a complex nonlinear relationship between extracted PPG features and pain status based on the numeric rating scale (NRS). A bagging ensemble model was used to improve classification performance. The DBN classifier showed better classification results than multilayer perceptron neural network (MLPNN) and support vector machine (SVM) models. In addition, the classification performance was improved when the selective bagging model was applied compared with the use of each single model classifier. The pain classifier based on DBN using a selective bagging model can be helpful in developing a pain classification system.

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15

Burton, R. M., and Dale A. Lundgren. "Wide Range Aerosol Classifier: A Size Selective Sampler for Large Particles." Aerosol Science and Technology 6, no. 3 (January 1987): 289–301. http://dx.doi.org/10.1080/02786828708959140.

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16

Xiong, Lin, Shasha Mao, and Licheng Jiao. "Selective Ensemble Based on Transformation of Classifiers Used SPCA." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 01 (January 4, 2015): 1550005. http://dx.doi.org/10.1142/s0218001415500056.

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The diversity and the accuracy are two important ingredients for ensemble generalization error in an ensemble classifiers system. Nevertheless enhancing the diversity is at the expense of decreasing the accuracy of classifiers, thus balancing the diversity and the accuracy is crucial for constructing a good ensemble method. In the paper, a new ensemble method is proposed that selecting classifiers to ensemble via the transformation of individual classifiers based on diversity and accuracy. In the proposed method, the transformation of classifiers is made to produce new individual classifiers based on original classifiers and the true labels, in order to enhance diversity of an ensemble. The transformation approach is similar to principal component analysis (PCA), but it is essentially different between them that the proposed method employs the true labels to construct the covariance matrix rather than the mean of samples in PCA. Then a selecting rule is constructed based on two rules of measuring the classification performance. By the selecting rule, some available new classifiers are selected to ensemble in order to ensure the accuracy of the ensemble with selected classifiers. In other words, some individuals with poor or same performance are eliminated. Particularly, a new classifier produced by the transformation is equivalent to a linear combination of original classifiers, which indicates that the proposed method enhances the diversity by different transformations instead of constructing different training subsets. The experimental results illustrate that the proposed method obtains the better performance than other methods, and the kappa-error diagrams also illustrate that the proposed method enhances the diversity compared against other methods.

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17

Verma, Pradeep, and Poornima Tyagi. "Credit Card Fraud Detection Using Selective Class Sampling and Random Forest Classifier." ECS Transactions 107, no. 1 (April 24, 2022): 4885–94. http://dx.doi.org/10.1149/10701.4885ecst.

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The incidences of credit card fraud are increasing. It puts the hard-earned money of users at risk. The financial institutions and governing bodies are looking forward to some robust and reliable ways of detecting credit card fraudulent transactions. The task is challenging due to non-availability of enough data, high class imbalance, and high stake on false negative rates (FNR). Most of the methods available in literature perform well on the accuracy-based performance metrics. However, they fail to yield satisfactory ROC-AUC performance due to the high false positive or false negative rates. Mitigating this issue is the real challenge for the task of fraudulent credit card transaction detection. This paper investigates the problem of credit card fraudulent transaction detection and proposes a technique for it. The proposed method uses a custom selective class sampling-based class balancing technique, and subsequently, it uses random forest for classification. The experimental results show that the proposed technique has better AUC score, accuracy, precision, and recall values as compared with other similar approaches.

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18

Chang, Jae-Young, and Han-Joon Kim. "Accelerating the EM Algorithm through Selective Sampling for Naive Bayes Text Classifier." KIPS Transactions:PartD 13D, no. 3 (June 1, 2006): 369–76. http://dx.doi.org/10.3745/kipstd.2006.13d.3.369.

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19

Chen, Jingnian, Houkuan Huang, Fengzhan Tian, and Shengfeng Tian. "A selective Bayes Classifier for classifying incomplete data based on gain ratio." Knowledge-Based Systems 21, no. 7 (October 2008): 530–34. http://dx.doi.org/10.1016/j.knosys.2008.03.013.

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20

Won, Kyungho, Moonyoung Kwon, Minkyu Ahn, and Sung Chan Jun. "Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI." Sensors 21, no. 16 (August 12, 2021): 5436. http://dx.doi.org/10.3390/s21165436.

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Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

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21

Lee, Chang-Hoon, and Soo-Young Lee. "Noise-Robust Speech Recognition Using Top-Down Selective Attention With an HMM Classifier." IEEE Signal Processing Letters 14, no. 7 (July 2007): 489–91. http://dx.doi.org/10.1109/lsp.2006.891326.

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22

Chen, Linlin, Mei Wang, Qiang Zhang, and Nan Hou. "GA-MKB:A Multi-kernel Boosting Learning Method based on Normalized Kernel Target Alignment and Kernel Difference." Journal of Physics: Conference Series 2281, no. 1 (June 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2281/1/012012.

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Abstract Concentrates on the problem that the traditional kernel target alignment(KTA) is not invariance under data translation in the feature space, a cosine matrix alignment method is proposed for kernel selection, which is called normalized kernel target alignment(NKTA). On the basis of normalized kernel target alignment and kernel difference, we propose a new multi-kernel boosting. Firstly, the value of NKTA is taken as the election rarget of the kernel function in each iteration of algorithm, which leads to a selective kernel fusion. Secondly, the kernel difference measure is used to construct the combination coefficient to increase the diversity of weak classifiers, and then improve the generalization performance of integrated strong classifiers. Finally, among the 6 data sets, the GA-MKB performed better than MKBoost-D1 under the accuracy of classification, and can improve the generalization performance of the integrated classifier compared with MKBoost-D2.

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23

Tian, Zhang, Chen, Geng, and Wang. "Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition." Sensors 19, no. 16 (August 8, 2019): 3468. http://dx.doi.org/10.3390/s19163468.

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

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24

Díaz-Amador, Roberto, and Miguel A. Mendoza-Reyes. "Towards the reduction of the effects of muscle fatigue on myoelectric control of upper limb prostheses." DYNA 86, no. 208 (January 1, 2019): 110–16. http://dx.doi.org/10.15446/dyna.v86n208.73401.

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This paper presents an investigation focused on the impact of muscle fatigue on a pattern recognition scheme for myoelectric control that uses three features sets and a Linear Discriminant Analysis classifier. Separability and repeatability between classes were used to evaluate the features changes while muscle fatigue was induced. Results show that while muscle fatigue is increasing over time, both separability and repeatability of the classes decrease. Finally two training schemes that use data acquired under fatigue, multiconditional training and selective classification, were evaluated using the Total Error Rate (TER). Results indicate that, when LDA classifier was trained whit no-fatigue, moderated fatigue and fatigue data, TER decreased to moderated and fatigue data, but increased to no-fatigue data. On the other hand, using three LDA classifiers to each of the condition, TER decreased to 9.26 % and 11 % in moderated fatigue and fatigue cases, while no-fatigue case was not affected.

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25

Barros, Murilo M. de, Fábio M. da Silva, Anderson G. Costa, Gabriel A. e. S. Ferraz, and Flávio C. da Silva. "Use of classifier to determine coffee harvest time by detachment force." Revista Brasileira de Engenharia Agrícola e Ambiental 22, no. 5 (May 2018): 366–70. http://dx.doi.org/10.1590/1807-1929/agriambi.v22n5p366-370.

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ABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.

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26

Jrad, Nisrine, Edith Grall-Maës, and Pierre Beauseroy. "Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections." Journal of Biomedicine and Biotechnology 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/608701.

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Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based onν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

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Zeineddine, Fadl, Benjamin Garmezy, Timothy A. Yap, and John Paul Y. C. Shen. "PMC: A more precise classifier of POLE mutations to identify candidates for immune therapy." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 3548. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.3548.

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3548 Background: Specific somatic mutations in DNA polymerase epsilon ( POLE) can cause a hypermutant phenotype with tumor mutation burden (TMB) in excess of 100 mutations per megabase. It has been reported that POLE mutant tumors are enriched in response to immune therapy and this association is being tested in multiple active clinical trials. However, most POLE mutations are passenger mutations and have no pathogenic role. Current methods to classify POLE mutations are limited in both accuracy and completeness, which could lead to inappropriate use of immune agents in tumor such as MSS CRC, where response rate is 5% or less. Here we present a new classifier, POLE Mutation Classifier or PMC, based on the unique trinucleotide mutation signature caused by selective loss of the proofreading function (LOP) of POLE. Methods: cBioPortal was queried to identify all tumors with POLE mutation. TMB was calculated for each, additionally, trinucleotide mutation signatures were obtained for all POLE mutant tumors in TCGA. Using OncoKB to identify a gold standard of 12 functional POLE mutations (n = 98 tumors) a POLE mutational signature was created. A combination of mutational signature, amino acid location, and TMB was used to classify each POLE variant. Results: Among all 48035 unique tumors the overall frequency of POLE mutations was 2.5% (n = 1184), however only 9.2% (n = 110) were determined to cause the selective LOP. The incidence of LOP POLE mutation was highest in uterine carcinoma and CRC, these tumors also had the highest ratio of LOP to passenger mutations. In a pan-cancer analysis the overall survival of LOP POLE patients was significantly better than those with passenger mutations (not-yet-reached vs. 51 mo, HR = 4.4, p < 0.0001). A similar analysis performed using the polyphen-2 classifier to identify functional POLE mutations did not show a difference in overall survival (HR = 1.0, p-value = 0.57). To further validate the improved specificity of the PMC classifier TMB was used as a surrogate marker, using the PMC classifier 98% of tumors with LOP showed hypermutation (TMB > 20mut/Mb), vs. 53% called functional by polyphen-2. A retrospective analysis of MD Anderson CRC patients identified 25 patients with LOP POLE mutation, who had improved OS relative to 267 CRC patients with passenger POLE mutation (not-yet-reached vs. 70 mo, HR:4.2, p = 0.028). Four metastatic CRC patients with LOP POLE mutation were treated with immune therapy (nivolumab, or ipilimumab/nivolumab) in 2nd or 3rd line, all four achieved objective response and remain on therapy (mean time on treatment 15 mo). Conclusions: The PMC classifier specifically identifies mutations in POLE that cause loss of the proofreading function, outperforming both manually curated databases and machine learning-based methods. Clinical trials that use POLE mutation as a selection criteria for immune therapy should be restricted to just those POLE mutations that cause LOP.

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Lin, Zhenqiang, Yiwen Lai, Taotao Pan, Wang Zhang, Jun Zheng, Xiaohong Ge, and Yuangang Liu. "A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision." Materials 14, no. 15 (July 27, 2021): 4175. http://dx.doi.org/10.3390/ma14154175.

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Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control.

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29

Gnip, Peter, Liberios Vokorokos, and Peter Drotár. "Selective oversampling approach for strongly imbalanced data." PeerJ Computer Science 7 (June 18, 2021): e604. http://dx.doi.org/10.7717/peerj-cs.604.

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Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.

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Hu, Yin E., and Ke Luo. "A Selective Naïve Bayesian Classification Algorithm Based on Rough Set." Applied Mechanics and Materials 325-326 (June 2013): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1593.

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Naive Bayesian classifier (NBC) is a simple and effective classification model, but its condition independence assumption is often violated in reality and makes it perform poorly. In our study, we attempt to improve the NBC model through the way of attribute selection based on rough set. The main idea of the improvement model is to select a closest approximate independent attributes subset and relax the assumption of independence. Through the experimental comparison and analysis on the UCI datasets, the model is proved effective.

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31

Chi, Cheng, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, and Xudong Zou. "Selective Refinement Network for High Performance Face Detection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8231–38. http://dx.doi.org/10.1609/aaai.v33i01.33018231.

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High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel twostep classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.

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Kamarulzaman, Aisyah Marliza Muhmad, Wan Shafrina Wan Mohd Jaafar, Khairul Nizam Abdul Maulud, Siti Nor Maizah Saad, Hamdan Omar, and Midhun Mohan. "Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery." Forests 13, no. 1 (January 2, 2022): 48. http://dx.doi.org/10.3390/f13010048.

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Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.

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Assumpção Silva, Ronan, Alceu S. Britto, Fabricio Enembreck, Robert Sabourin, and Luiz S. Oliveira. "Selecting and Combining Classifiers Based on Centrality Measures." International Journal on Artificial Intelligence Tools 29, no. 03n04 (June 2020): 2060004. http://dx.doi.org/10.1142/s0218213020600040.

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Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’s accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases.

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Sourov, Injamamul Haque, Faiyaz Alvi Ahmed, Md Tawhid Islam Opu, Aunnoy K. Mutasim, M. Raihanul Bashar, Rayhan Sardar Tipu, Md Ashraful Amin, and Md Kafiul Islam. "EEG-Based Preference Classification for Neuromarketing Application." Computational Intelligence and Neuroscience 2023 (March 1, 2023): 1–13. http://dx.doi.org/10.1155/2023/4994751.

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Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

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Wirth, Lori J., Mimi I.-Nan Hu, Steven G. Waguespack, Chrysoula Dosiou, Paul Ladenson, Masha J. Livhits, Peter M. Sadow, et al. "NTRK, RET, BRAF, and ALK fusions in thyroid fine-needle aspirates (FNAs)." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 6083. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6083.

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6083 Background: Receptor tyrosine kinase (RTK) fusions may be targeted by small molecule inhibitors to treat various advanced tumors, including thyroid cancer. Clinical trials have studied selective inhibitors of ALK, BRAF, NTRK and RET, leading to several FDA-approved therapies. The Afirma Genomic Sequencing Classifier (GSC) classifies cytologically indeterminate thyroid nodules as molecularly benign or suspicious. The Xpression Atlas reports 905 genomic variants and 235 fusion pairs on GSC Suspicious, Suspicious for Malignancy (SFM), and Malignant FNA samples at the time of diagnosis. Here we report the prevalence of these fusion genes in real-world clinical practice. Methods: We analyzed anonymized data from 50,644 consecutive Bethesda III-VI nodule FNA samples submitted to the Veracyte CLIA laboratory for molecular testing using whole transcriptome RNA sequencing (RNA-Seq). Gene pairs are listed alphabetically. Results: 32,080 Bethesda III/IV nodules were classified as GSC Benign and 278 were Parathyroid Classifier positive. No ALK, BRAF, NTRK1/3, or RET fusions were identified among these samples. Among 16,594 Bethesda III/IV GSC Suspicious FNAs, 3% (n = 529) were positive for ALK, BRAF, NTRK1/3 or RET fusions. Among the 1,692 Bethesda V/VI FNAs, the proportion of positive nodules was 8% (n = 135). Among these combined cohorts of Bethesda III/IV GSC Suspicious and Bethesda V/VI, the most common gene fusions observed for each of the 5 studied RTK genes was: ETV6/NTRK3 (n = 164, 72% of NTRK3 fusions), CCDC6/RET (n = 104, 55% of RET), BRAF/SND1 (n = 32, 20% of BRAF), ALK/STRN (n = 20, 37% of ALK), and NTRK1/TPM3 (n = 14, 50% of NTRK1). BRAF showed the highest diversity of fusions, with 80 gene partners. Different gene partners with RET, ALK, NTRK1, and NTRK3 numbered 25, 11, 9, and 5 , respectively . Conclusions: Whole-transcriptome RNA-seq on small sample thyroid FNA specimens can identify clinically relevant ALK, BRAF, NTRK, and RET fusions across Bethesda categories. The prevalence ranges from 3% in Bethesda III/IV Afirma GSC Suspicious specimens to 8% among Bethesda V/VI specimens. Future studies need to determine if detection of precision medicine candidates by pre-operative FNA can optimize initial treatment, predict response to treatment, and prioritize selective targeted therapy should systemic treatment be needed.[Table: see text]

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Ghosh, Partha, Arpan Losalka, and Michael J. Black. "Resisting Adversarial Attacks Using Gaussian Mixture Variational Autoencoders." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 541–48. http://dx.doi.org/10.1609/aaai.v33i01.3301541.

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Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, “adversarial samples” and “fooling samples”, have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.1

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37

Liu, Hui. "An Unsupervised Classification Method of Multi-Polarization Synthetic Aperture Radar Imagery." Journal of Nanoelectronics and Optoelectronics 17, no. 1 (January 1, 2022): 48–55. http://dx.doi.org/10.1166/jno.2022.3168.

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In this paper, a clustering method that combines a polarization scattering mechanism with the statistical properties of polarization data is proposed aiming to improve the accuracy of SAR image classification. It is based on the decomposition algorithm of Freeman-Durden scattering model and the non-Gaussian K-Wishart distribution classifier. A modified selective and Markov random field-based adaptive ability classification algorithm can be used especially for complex geomorphological environment conditions. The experimental result shows that the correction classifier iterative algorithm based on the Markov random field are highly useful for the uneven distribution area. It is possible that the method will contribute to reduce the computational complexity of PolSAR classification process and provide higher accuracy.

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38

Cong, Jinyu, Benzheng Wei, Yunlong He, Yilong Yin, and Yuanjie Zheng. "A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis." Computational and Mathematical Methods in Medicine 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/4896386.

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Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

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Zhu, Shaolong, Maoni Chao, Jinyu Zhang, Xinjuan Xu, Puwen Song, Jinlong Zhang, and Zhongwen Huang. "Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology." Sensors 19, no. 23 (November 28, 2019): 5225. http://dx.doi.org/10.3390/s19235225.

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Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

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Hewahi, Nabil M., and Sarah N. Kohail. "Learning Concept Drift Using Adaptive Training Set Formation Strategy." International Journal of Technology Diffusion 4, no. 1 (January 2013): 33–55. http://dx.doi.org/10.4018/jtd.2013010103.

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We live in a dynamic world, where changes are a part of everyday life. When there is a shift in data, the classification or prediction models need to be adaptive to the changes. In data mining the phenomenon of change in data distribution over time is known as concept drift. In this research, the authors propose an adaptive supervised learning with delayed labeling methodology. As a part of this methodology, the atuhors introduce Adaptive Training Set Formation for Delayed Labeling Algorithm (SFDL), which is based on selective training set formation. Our proposed solution is considered as the first systematic training set formation approach which takes into account delayed labeling problem. It can be used with any base classifier without the need to change the implementation or setting of this classifier. The authors test their algorithm implementation using synthetic and real dataset from various domains which might have different drift types (sudden, gradual, incremental recurrences) with different speed of change. The experimental results confirm improvement in classification accuracy as compared to ordinary classifier for all drift types. The authors’ approach is able to increase the classifications accuracy with 20% in average and 56% in the best cases of our experimentations and it has not been worse than the ordinary classifiers in any case. Finally a comparison with other four related methods to deal with changing in user interest over time and handle recurrence drift is performed. These methods are simple incremental method, time window approach with different window size, instance weighting method and conceptual clustering and prediction framework (CCP). Results indicate the effectiveness of the proposed method over other methods in terms of classification accuracy.

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41

Choi, Sang-Il, Sang Tae Choi, and Haanju Yoo. "Selective Feature Generation Method for Classification of Low-Dimensional Data." International Journal of Computers Communications & Control 13, no. 1 (February 12, 2018): 24. http://dx.doi.org/10.15837/ijccc.2018.1.2931.

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We propose a method that generates input features to effectively classify low-dimensional data. To do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the ‘discrimination distance’ for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features.

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Hayat, Muhammad Badar, Muhammad Danishwar, Amna Hamid, Mirza Muhammad Zaid, and Muhammad Zaka Emad. "Quadratic Mathematical Modeling of Sustainable Dry Beneficiation of Kaolin." Minerals 11, no. 4 (April 18, 2021): 429. http://dx.doi.org/10.3390/min11040429.

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Clay minerals are one of the most utilized minerals among non-metals. These are hydrous aluminum silicates with a layer (sheet-like) structure. Kaolin is a hydrous aluminosilicate mineral with a thin platelet structure. Kaolin is extensively used in paper, paint, and many other industries. Wet processing of kaolin will not be sustainable over the long term because global freshwater resources are becoming scarce. Hence, a process is necessary that does not consume water during the beneficiation of kaolin. This study developed a dry beneficiation process for low-grade kaolin of 59.6%, with 12% quartz and about 6% titaniferous impurities from Nagar Parkar, Sindh province, Pakistan. To develop a size difference between kaolinite and impurities, steel balls clad with rubber were used as the grinding media in a selective grinding unit. Screens of 60 and 400 mesh were employed to classify the feed of air classifier. Oversize +60 mesh was reground, 400 to 60 mesh fractions were sent to an air classifier, and −400 mesh was considered to be a product with the grade and recovery of 90.6% and 20.5%, respectively. Air classifier experiments were designed using central composite design. An experiment using a fan speed of 1200 revolutions per minute (rpm) and a shutter opening of 4.0 showed optimum results, with maximum kaolinite grade and recovery of 91.5% and 35.9%, respectively. The statistical models developed for grade and recovery predicted the optimum results at a fan speed of 1251 rpm and shutter opening of 3.3 with the maximum kaolinite grade and recovery of 91.1% and 24.7%, respectively. The differences between experimental and predicted grade and recovery were 0.1% and 2.4%, respectively. The characterization results showed the total upgrade of kaolin from 59.6% to 91.2%, with 27.1% recovery during the process. The designed methodology has the potential to improve the yield of the product by focusing on its recovery. Furthermore, the designed process can be improved by using different sized balls in the selective grinding unit. This beneficiation process can utilize more than one air classifier in series to achieve the targeted results.

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43

Hsieh, Miao-Ling, and Su-Ying Hsiao. "On the “one+verbal classifier” sequence as a delimitative aspect marker in Taiwanese Southern Min." Language and Linguistics / 語言暨語言學 23, no. 4 (September 12, 2022): 680–709. http://dx.doi.org/10.1075/lali.00119.hsi.

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Abstract This paper studies the “one+verbal classifier” sequence tsi̍t-ē that appears after an indefinite object complement in Taiwanese Southern Min. We call it the post-complement (PC) tsi̍t-ē. While the tsi̍t-ē sequence can be a durative phrase when it is immediately preceded by a verb, the PC tsi̍t-ē cannot be replaced by the durative phrase tsi̍t-ē-á ‘a while’ (tsi̍t-ē plus the diminutive suffix á) or other durative phrases. We show that the PC tsi̍t-ē is a sentence-final particle, not a durative phrase serving as a predicate or complement. Moreover, it marks delimitativity, which means ‘termination in a short time.’ It is the same kind of delimitativity that verb reduplication in Mandarin Chinese expresses despite the fact that the latter targets on the verb and is more selective in terms of the verb types that it can occur with. Moreover, the PC tsi̍t-ē carries the ‘down-play’ meaning. Syntactically, we suggest that it heads an AspP, which occurs above a vP.

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44

Dadashzadeh, Mojtaba, Yousef Abbaspour-Gilandeh, Tarahom Mesri-Gundoshmian, Sajad Sabzi, José Luis Hernández-Hernández, Mario Hernández-Hernández, and Juan Ignacio Arribas. "Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields." Plants 9, no. 5 (April 27, 2020): 559. http://dx.doi.org/10.3390/plants9050559.

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Site-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively.

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Kapoor, Rajiv, Om Mishra, and Madan Mohan Tripathi. "Human action recognition using descriptor based on selective finite element analysis." Journal of Electrical Engineering 70, no. 6 (December 1, 2019): 443–53. http://dx.doi.org/10.2478/jee-2019-0077.

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Abstract This paper proposes a novel local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the distinctive human poses in the form of the stiffness matrix. This stiffness matrix gives the information of motion as well as shape change of the human body while performing an action. Initially, the human body is represented in the silhouette form. Most prominent points of the silhouette are then selected. This silhouette is discretized into several finite small triangle faces (elements) where the prominent points of the boundaries are the vertices of the triangles. The stiffness matrix of each triangle is then calculated. The feature vector representing the action video frame is constructed by combining all stiffness matrices of all possible triangles. These feature vectors are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The proposed method shows its superiority over other existing state-of-the-art methods on the challenging datasets Weizmann, KTH, Ballet, and IXMAS.

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46

Jenset, Gard B., and Barbara McGillivray. "Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model." Machine Learning and Knowledge Extraction 1, no. 2 (April 18, 2019): 630–40. http://dx.doi.org/10.3390/make1020037.

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Natural Language Understanding (NLU) systems are essential components in many industry conversational artificial intelligence applications. There are strong incentives to develop a good NLU capability in such systems, both to improve the user experience and in the case of regulated industries for compliance reasons. We report on a series of experiments comparing the effects of optimizing word embeddings versus implementing a multi-classifier ensemble approach and conclude that in our case, only the latter approach leads to significant improvements. The study provides a high-level primer for developing NLU systems in regulated domains, as well as providing a specific baseline accuracy for evaluating NLU systems for financial guidance.

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47

Kavitha, P., and M. Prabakaran. "An Efficient Tweeter Sentiment Analysis Sfcetr Selective Feature Based Case Content Extraction Using Maximum Entropy Classifier To Rank The Tweets." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 289–99. http://dx.doi.org/10.26438/ijcse/v6i9.289299.

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48

Koyuncu, Hasan, and Mücahid Barstuğan. "COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier." Signal Processing: Image Communication 97 (September 2021): 116359. http://dx.doi.org/10.1016/j.image.2021.116359.

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49

Hao, Lijun, Min Zhang, and Gang Huang. "Feature Optimization of Exhaled Breath Signals Based on Pearson-BPSO." Mobile Information Systems 2021 (December 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/1478384.

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Feature optimization, which is the theme of this paper, is actually the selective selection of the variables on the input side at the time of making a predictive kind of model. However, an improved feature optimization algorithm for breath signal based on the Pearson-BPSO was proposed and applied to distinguish hepatocellular carcinoma by electronic nose (eNose) in the paper. First, the multidimensional features of the breath curves of hepatocellular carcinoma patients and healthy controls in the training samples were extracted; then, the features with less relevance to the classification were removed according to the Pearson correlation coefficient; next, the fitness function was constructed based on K-Nearest Neighbor (KNN) classification error and feature dimension, and the feature optimization transformation matrix was obtained based on BPSO. Furthermore, the transformation matrix was applied to optimize the test sample’s features. Finally, the performance of the optimization algorithm was evaluated by the classifier. The experiment results have shown that the Pearson-BPSO algorithm could effectively improve the classification performance compared with BPSO and PCA optimization methods. The accuracy of SVM and RF classifier was 86.03% and 90%, respectively, and the sensitivity and specificity were about 90% and 80%. Consequently, the application of Pearson-BPSO feature optimization algorithm will help improve the accuracy of hepatocellular carcinoma detection by eNose and promote the clinical application of intelligent detection.

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50

Tian, Yiming, Jie Zhang, Qi Chen, Shuping Hou, and Li Xiao. "Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks." Sensors 22, no. 21 (October 27, 2022): 8225. http://dx.doi.org/10.3390/s22218225.

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Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature.

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