Journal articles: 'Discriminative classifier'

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Tan, Alan W. C., M. V. C. Rao, and B. S. Daya Sagar. "A Discriminative Signal Subspace Speech Classifier." IEEE Signal Processing Letters 14, no. 2 (February 2007): 133–36. http://dx.doi.org/10.1109/lsp.2006.882091.

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Hassan, Anthony Rotimi, Rasaki Olawale Olanrewaju, Queensley C. Chukwudum, Sodiq Adejare Olanrewaju, and S. E. Fadugba. "Comparison Study of Generative and Discriminative Models for Classification of Classifiers." International Journal of Mathematics and Computers in Simulation 16 (June 28, 2022): 76–87. http://dx.doi.org/10.46300/9102.2022.16.12.

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In classification of classifier analysis, researchers have been worried about the classifier of existing generative and discriminative models in practice for analyzing attributes data. This makes it necessary to give an in-depth, systematic, interrelated, interconnected, and classification of classifier of generative and discriminative models. Generative models of Logistic and Multinomial Logistic regression models and discriminative models of Linear Discriminant Analysis (LDA) (for attribute P=1 and P>1), Quadratic Discriminant Analysis (QDA) and Naïve Bayes were thoroughly dealt with analytically and mathematically. A step-by-step empirical analysis of the mentioned models were carried-out via chemical analysis of wines grown in a region in Italy that was derived from three different cultivars (The three types of wines that constituted the three different cultivars or three classifiers). Naïve Bayes Classifier set the pace via leading a-prior probabilities.

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Hu, Kai-Jun, He-Feng Yin, and Jun Sun. "Discriminative non-negative representation based classifier for image recognition." Journal of Algorithms & Computational Technology 15 (January 2021): 174830262110449. http://dx.doi.org/10.1177/17483026211044922.

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During the past decade, representation based classification method has received considerable attention in the community of pattern recognition. The recently proposed non-negative representation based classifier achieved superb recognition results in diverse pattern classification tasks. Unfortunately, discriminative information of training data is not fully exploited in non-negative representation based classifier, which undermines its classification performance in practical applications. To address this problem, we introduce a decorrelation regularizer into the formulation of non-negative representation based classifier and propose a discriminative non-negative representation based classifier for pattern classification. The decorrelation regularizer is able to reduce the correlation of representation results of different classes, thus promoting the competition among them. Experimental results on benchmark datasets validate the efficacy of the proposed discriminative non-negative representation based classifier, and it can outperform some state-of-the-art deep learning based methods. The source code of our proposed discriminative non-negative representation based classifier is accessible at https://github.com/yinhefeng/DNRC .

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SHI, Hong-bo, Ya-qin LIU, and Ai-jun LI. "Discriminative parameter learning of Bayesian network classifier." Journal of Computer Applications 31, no. 4 (June 9, 2011): 1074–78. http://dx.doi.org/10.3724/sp.j.1087.2011.01074.

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Devi, Rajkumari Bidyalakshmi, Yambem Jina Chanu, and Khumanthem Manglem Singh. "Incremental visual tracking via sparse discriminative classifier." Multimedia Systems 27, no. 2 (January 18, 2021): 287–99. http://dx.doi.org/10.1007/s00530-020-00748-4.

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Tang, Hui, and Kui Jia. "Discriminative Adversarial Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5940–47. http://dx.doi.org/10.1609/aaai.v34i04.6054.

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Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classifier that can well classify target instances. Recent advances rely on domain-adversarial training of deep networks to learn domain-invariant features. However, due to an issue of mode collapse induced by the separate design of task and domain classifiers, these methods are limited in aligning the joint distributions of feature and category across domains. To overcome it, we propose a novel adversarial learning method termed Discriminative Adversarial Domain Adaptation (DADA). Based on an integrated category and domain classifier, DADA has a novel adversarial objective that encourages a mutually inhibitory relation between category and domain predictions for any input instance. We show that under practical conditions, it defines a minimax game that can promote the joint distribution alignment. Except for the traditional closed set domain adaptation, we also extend DADA for extremely challenging problem settings of partial and open set domain adaptation. Experiments show the efficacy of our proposed methods and we achieve the new state of the art for all the three settings on benchmark datasets.

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Ropelewska, Ewa. "The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium." Agricultural Engineering 22, no. 3 (September 1, 2018): 49–56. http://dx.doi.org/10.1515/agriceng-2018-0026.

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AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

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Ćwiklińska-Jurkowska, Małgorzata M. "Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression." Studies in Logic, Grammar and Rhetoric 43, no. 1 (December 1, 2015): 73–99. http://dx.doi.org/10.1515/slgr-2015-0043.

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Abstract A gene expression data set, containing 3051 genes and 38 tumor mRNA training samples, from a leukemia microarray study, was used for differentiation between ALL and AML groups of leukemia. In this paper, single and combined discriminant methods were applied on the basis of the selected few most discriminative variables according to Wilks’ lambda or the leave-one-out error of first nearest neighbor classifier. For the linear, quadratic, regularized, uncorrelated discrimination, kernel, nearest neighbor and naive Bayesian classifiers, two-dimensional graphs of the boundaries and discriminant functions for diagnostics are presented. Cross-validation and leave-one-out errors were used as measures of classifier performance to support diagnosis coming from this genomic data set. A small number of best discriminating genes, from two to ten, was sufficient to build discriminant methods of good performance. Especially useful were nearest neighbor methods. The results presented herein were comparable with outcomes obtained by other authors for larger numbers of applied genes. The linear, quadratic, uncorrelated Bayesian and regularized discrimination methods were subjected to bagging or boosting in order to assess the accuracy of the fusion. A conclusion drawn from the analysis was that resampling ensembles were not beneficial for two-dimensional discrimination.

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Prevost, Lionel, Loïc Oudot, Alvaro Moises, Christian Michel-Sendis, and Maurice Milgram. "Hybrid generative/discriminative classifier for unconstrained character recognition." Pattern Recognition Letters 26, no. 12 (September 2005): 1840–48. http://dx.doi.org/10.1016/j.patrec.2005.03.005.

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Ahmadi, Ehsan, Zohreh Azimifar, Maryam Shams, Mahmoud Famouri, and Mohammad Javad Shafiee. "Document image binarization using a discriminative structural classifier." Pattern Recognition Letters 63 (October 2015): 36–42. http://dx.doi.org/10.1016/j.patrec.2015.06.008.

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Hou, Mingzhen, Wei Xia, Xiangdong Zhang, and Quanxue Gao. "Discriminative comparison classifier for generalized zero-shot learning." Neurocomputing 414 (November 2020): 10–17. http://dx.doi.org/10.1016/j.neucom.2020.07.030.

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Ouamour, Siham, and Halim Sayoud. "Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers." International Journal of Mobile Computing and Multimedia Communications 1, no. 4 (October 2009): 47–63. http://dx.doi.org/10.4018/jmcmc.2009072804.

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This article describes a new Speaker Discrimination System (SDS), which is a part of an overall project called Audio Documents Indexing based on a Speaker Discrimination System (ADISDS). Speaker discrimination consists in checking whether two speech segments come from the same speaker or not. This research domain presents an important field in biometry, since the voice remains an important feature used at distance (via telephone). However, although some discriminative classifiers do exist nowadays, their performances are not enough sufficient for short speech segments. This issue led us to propose an efficient fusion between such classifiers in order to enhance the discriminative performance. This fusion is obtained, by using three different techniques: a serial fusion, parallel fusion and serial-parallel fusion. Also, two classifiers have been chosen for the evaluation: a mono-gaussian statistical classifier and a Multi Layer Perceptron (MLP). Several experiments of speaker discrimination are conducted on different databases: Hub4 Broadcast-News and telephonic calls. Results show that the fusion has efficiently improved the scores obtained by each approach alone. So, for instance, we got an Equal Error Rate (EER) of about 7% on a subset of Hub4 Broadcast-News database, with short segments of 4 seconds, and an EER of about 4% on telephonic speech, with medium segments of 10 seconds.

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Baayen, R. Harald. "Corpus linguistics and naive discriminative learning." Revista Brasileira de Linguística Aplicada 11, no. 2 (2011): 295–328. http://dx.doi.org/10.1590/s1984-63982011000200003.

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Three classifiers from machine learning (the generalized linear mixed model, memory based learning, and support vector machines) are compared with a naive discriminative learning classifier, derived from basic principles of error-driven learning characterizing animal and human learning. Tested on the dative alternation in English, using the Switchboard data from (BRESNAN; CUENI; NIKITINA; BAAYEN, 2007), naive discriminative learning emerges with stateof-the-art predictive accuracy. Naive discriminative learning offers a united framework for understanding the learning of probabilistic distributional patterns, for classification, and for a cognitive grounding of distinctive collexeme analysis.

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ZHANG, RUI, XIAO QING DING, and HAI LONG LIU. "DISCRIMINATIVE TRAINING BASED QUADRATIC CLASSIFIER FOR HANDWRITTEN CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 06 (September 2007): 1035–46. http://dx.doi.org/10.1142/s0218001407005776.

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In offline handwritten character recognition, the classifier with modified quadratic discriminant function (MQDF) has achieved good performance. The parameters of MQDF classifier are commonly estimated by the maximum likelihood (ML) estimator, which maximizes the within-class likelihood instead of directly minimizing the classification errors. To improve the performance of MQDF classifier, in this paper, the MQDF parameters are revised by discriminative training using a minimum classification error (MCE) criterion. The proposed algorithm is applied to recognizing handwritten numerals and handwritten Chinese characters, the recognition rates obtained are among the highest that have ever been reported.

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Chiu, Hui-Chu, Yao-Hsien Lee, Chih-Cheng Wang, Chen-Shu Wang, Chi-Chung Lee, Ming-Hsiung Ying, Mei-Yu Wu, Wen-Chih Chang, and Deng-Yiv Chiu. "To Explore Intracerebral Hematoma with a Hybrid Approach and Combination of Discriminative Factors." Methods of Information in Medicine 55, no. 05 (2016): 450–54. http://dx.doi.org/10.3414/me15-01-0137.

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SummaryObjectives: To find discriminative combination of influential factors of Intracerebral hematoma (ICH) to cluster ICH patients with similar features to explore relationship among influential factors and 30-day mortality of ICH. Methods: The data of ICH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster ICH patients with similar features using Fuzzy C-means algorithm (FCM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. Results: The two influential factors chosen to split the decision tree are Glasgow coma scale (GCS) score and Hematoma size. FCM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FCM algorithm to cluster training data. Conclusions: It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable.

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Zan Baofeng, 昝宝锋, 孔军 Kong Jun, and 蒋敏 Jiang Min. "Human Action Recognition Based on Discriminative Collaborative Representation Classifier." Laser & Optoelectronics Progress 55, no. 1 (2018): 011010. http://dx.doi.org/10.3788/lop55.011010.

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Kong, Deming, Liangliang Duan, Peiliang Wu, and Wenji Yang. "Salient Region Detection via Feature Combination and Discriminative Classifier." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/846895.

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We introduce a novel approach to detect salient regions of an image via feature combination and discriminative classifier. Our method, which is based on hierarchical image abstraction, uses the logistic regression approach to map the regional feature vector to a saliency score. Four saliency cues are used in our approach, including color contrast in a global context, center-boundary priors, spatially compact color distribution, and objectness, which is as an atomic feature of segmented region in the image. By mapping a four-dimensional regional feature to fifteen-dimensional feature vector, we can linearly separate the salient regions from the clustered background by finding an optimal linear combination of feature coefficients in the fifteen-dimensional feature space and finally fuse the saliency maps across multiple levels. Furthermore, we introduce the weighted salient image center into our saliency analysis task. Extensive experiments on two large benchmark datasets show that the proposed approach achieves the best performance over several state-of-the-art approaches.

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Lin, Huiping, Hang Chen, Hongmiao Wang, Junjun Yin, and Jian Yang. "Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning." Remote Sensing 11, no. 7 (March 29, 2019): 769. http://dx.doi.org/10.3390/rs11070769.

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Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.

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Tamchyna, Aleš, Fabienne Braune, Alexander Fraser, Marine Carpuat, Hal Daumé iii, and Chris Quirk. "Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding." Prague Bulletin of Mathematical Linguistics 101, no. 1 (April 1, 2014): 29–41. http://dx.doi.org/10.2478/pralin-2014-0002.

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Abstract Current state-of-the-art statistical machine translation (SMT) relies on simple feature functions which make independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule, which is available at decoding time. We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.

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Haga, Takeshi, Hiroshi Kera, and Kazuhiko Kawamoto. "Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement." Sensors 23, no. 5 (February 24, 2023): 2515. http://dx.doi.org/10.3390/s23052515.

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In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos. Building sequential variational autoencoders with a two-stream architecture induces inductive bias for video disentanglement. However, our preliminary experiment demonstrated that the two-stream architecture is insufficient for video disentanglement because static features frequently contain dynamic features. Additionally, we found that dynamic features are not discriminative in the latent space. To address these problems, we introduced an adversarial classifier using supervised learning into the two-stream architecture. The strong inductive bias through supervision separates dynamic features from static features and yields discriminative representations of the dynamic features. Through a comparison with other sequential variational autoencoders, we qualitatively and quantitatively demonstrate the effectiveness of the proposed method on the Sprites and MUG datasets.

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Ramos, Guilherme N., Fangyan Dong, and Kaoru Hirota. "HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 338–46. http://dx.doi.org/10.20965/jaciii.2009.p0338.

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A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.

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Fujino, Akinori, Naonori Ueda, and Kazumi Saito. "A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing." Transactions of the Japanese Society for Artificial Intelligence 21 (2006): 301–9. http://dx.doi.org/10.1527/tjsai.21.301.

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Zhang, Zhao, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, and Shuicheng Yan. "Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier." IEEE Transactions on Neural Networks and Learning Systems 29, no. 8 (August 2018): 3798–814. http://dx.doi.org/10.1109/tnnls.2017.2740224.

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Tabibian, Shima, Ahmad Akbari, and Babak Nasersharif. "Keyword spotting using an evolutionary-based classifier and discriminative features." Engineering Applications of Artificial Intelligence 26, no. 7 (August 2013): 1660–70. http://dx.doi.org/10.1016/j.engappai.2013.03.009.

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Kumar, Parmod, D. Suganthi, K. Valarmathi, Mahendra Pratap Swain, Piyush Vashistha, Dharam Buddhi, and Emmanuel Sey. "A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models." BioMed Research International 2023 (February 6, 2023): 1–9. http://dx.doi.org/10.1155/2023/5803661.

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Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians’ support to spot the affected regions. Modern healthcare systems develop an easy way to diagnose the disease. Discriminative architectures in deep learning can be viewed as supervised deep learning algorithms which use classification/regression techniques to predict the output. A convolutional neural network (CNN) is a part of the discriminative architecture which helps to process both image and text data. This work suggests the CNN-based classifier which classifies the tumor and nontumor regions in retinoblastoma. The tumor-like region (TLR) in retinoblastoma is identified using the automated thresholding method. After that, ResNet and AlexNet algorithms are used to classify the cancerous region along with classifiers. In addition, the comparison of discriminative algorithm along with its variants is experimented to produce the better image analysis method without the intervention of clinicians. The experimental study reveals that ResNet50 and AlexNet yield better results compared to other learning modules.

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Tantawi, Manal, Aya Naser, Howida Shedeed, and Mohammed Fahmy Tolba. "Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy." International Journal of Service Science, Management, Engineering, and Technology 12, no. 3 (May 2021): 96–115. http://dx.doi.org/10.4018/ijssmet.2021050106.

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Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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Wang, Limin, Yang Liu, Musa Mammadov, Minghui Sun, and Sikai Qi. "Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance." Entropy 21, no. 5 (May 13, 2019): 489. http://dx.doi.org/10.3390/e21050489.

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Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.

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FAUST, OLIVER, PENG CHUAN ALVIN ANG, SUBHA D. PUTHANKATTIL, and PAUL K. JOSEPH. "DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES." Journal of Mechanics in Medicine and Biology 14, no. 03 (March 13, 2014): 1450035. http://dx.doi.org/10.1142/s0219519414500353.

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Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy ( P h). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.

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Xu, Tingting, Ye Zhao, and Xueliang Liu. "Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning." Complexity 2021 (February 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/6656797.

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Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets (AWA1, AWA2, APY, FLO, SUN, and CUB). The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning.

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Yang, Jian, Delin Chu, Lei Zhang, Yong Xu, and Jingyu Yang. "Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition." IEEE Transactions on Neural Networks and Learning Systems 24, no. 7 (July 2013): 1023–35. http://dx.doi.org/10.1109/tnnls.2013.2249088.

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Zhang, Zhenxin, Liqiang Zhang, Yumin Tan, Liang Zhang, Fangyu Liu, and Ruofei Zhong. "Joint Discriminative Dictionary and Classifier Learning for ALS Point Cloud Classification." IEEE Transactions on Geoscience and Remote Sensing 56, no. 1 (January 2018): 524–38. http://dx.doi.org/10.1109/tgrs.2017.2751061.

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Tabibian, Shima, Ahmad Akbari, and Babak Nasersharif. "Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting." Neural Processing Letters 39, no. 2 (April 18, 2013): 195–218. http://dx.doi.org/10.1007/s11063-013-9299-4.

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Chen, Fuzan, Harris Wu, Runliang Dou, and Minqiang Li. "A high-dimensional classification approach based on class-dependent feature subspace." Industrial Management & Data Systems 117, no. 10 (December 4, 2017): 2325–39. http://dx.doi.org/10.1108/imds-11-2016-0491.

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Purpose The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification. Design/methodology/approach A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators. Findings Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data. Research limitations/implications UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study. Practical implications The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data. Originality/value The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.

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Georgoulaki, Kristina. "Classification of Pointillist paintings using colour and texture features." International Journal of Electrical and Computer Engineering Research 2, no. 1 (March 15, 2022): 13–19. http://dx.doi.org/10.53375/ijecer.2022.208.

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Fine art paintings classification based on artistic style is a field of growing interest. Pointillist style is one of the most easily recognized painting styles by humans, due to its characteristic tiny detached paintbrushes of pure colour. In this paper automatic discrimination of artworks belonging to the style of Pointillism is investigated. The opposite styles considered are Cubism, Purism, Naïve art and Impressionism. Several colour and texture features are considered and a feature selection procedure is employed to reveal the most relevant ones to pointillist movement. Binary classification is performed, both in supervised and unsupervised mode, to assess the features’ discriminative ability. A small number of selected features is shown, by simulations results, to be quite powerful predictors resulting in a classification accuracy of 94% for a SVM classifier, 93.5% for a KNN classifier and 87% for a k-means classifier.

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Li, Zexin, Kaiji Yang, Lili Zhang, Chiju Wei, Peixuan Yang, and Wencan Xu. "Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder." International Journal of Endocrinology 2020 (December 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/9015713.

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Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.

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Li, Jun Shan, Fang Zhou Zhao, Ying Hong Zhu, and Wei Yang. "Infrared Object Tracking Algorithm Based on Online AdaBoost." Advanced Materials Research 443-444 (January 2012): 447–51. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.447.

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An online AdaBoost based tracking algorithm in FLIR imagery is proposed, where tracking is formulated as a binary classification problem. The object features are selected adaptively via online boosting. And then, a strong classifier is built on the weak classifiers. The confidence map of consecutive image frame is created by the strong classifier. The object localization is realized by detecting maximum of the confidence map using mean shift. The weak classfiers are updated according to the new samples to improve the discriminative ability to the object appearance and complex scene. Experiment results verify the effectives and robustness of this tracking algorithm which can improve the tracking performance efficiently.

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Ropelewska, Ewa, Xiang Cai, Zhan Zhang, Kadir Sabanci, and Muhammet Fatih Aslan. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels." Agriculture 12, no. 2 (February 17, 2022): 285. http://dx.doi.org/10.3390/agriculture12020285.

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Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels.

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Pasaribu, Novie Theresia Br, and M. Jimmy Hasugian. "Feature Extraction Comparison in Handwriting Recognition of Batak Toba Alphabet." IJITEE (International Journal of Information Technology and Electrical Engineering) 1, no. 3 (January 4, 2018): 86. http://dx.doi.org/10.22146/ijitee.31969.

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Offline handwriting recognition is one of the most prominent research topics due to its tremendous application and high variability as well. This paper covers the offline Batak Toba handwritten text recognition, from the noise removal, the process of feature extraction until the recognition by using several classifiers. Experiments show that elliptic fourier descriptor (EFD) is the most discriminative feature and Mahalanobis distance (MD) outperforms the two others classifier.

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Cho, Sung-Bae, and Jin H. Kim. "An HMM/MLP Architecture for Sequence Recognition." Neural Computation 7, no. 2 (March 1995): 358–69. http://dx.doi.org/10.1162/neco.1995.7.2.358.

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This paper presents a hybrid architecture of hidden Markov models (HMMs) and a multilayer perceptron (MLP). This exploits the discriminative capability of a neural network classifier while using HMM formalism to capture the dynamics of input patterns. The main purpose is to improve the discriminative power of the HMM-based recognizer by additionally classifying the likelihood values inside them with an MLP classifier. To appreciate the performance of the presented method, we apply it to the recognition problem of on-line handwritten characters. Simulations show that the proposed architecture leads to a significant improvement in generalization performance over conventional approaches to sequential pattern recognition.

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Lewin, Jeremy, Paul Dufort, Jaydeep Halankar, Martin O’Malley, Michael A. S. Jewett, Robert J. Hamilton, Abha Gupta, et al. "Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–12. http://dx.doi.org/10.1200/cci.18.00004.

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Purpose After chemotherapy, approximately 50% of patients with metastatic testicular germ cell tumors (GCTs) who undergo retroperitoneal lymph node dissections (RPNLDs) for residual masses have fibrosis. Radiomics uses image processing techniques to extract quantitative textures/features from regions of interest (ROIs) to train a classifier that predicts outcomes. We hypothesized that radiomics would identify patients with a high likelihood of fibrosis who may avoid RPLND. Patients and Methods Patients with GCT who had an RPLND for nodal masses > 1 cm after first-line platinum chemotherapy were included. Preoperative contrast-enhanced axial computed tomography images of retroperitoneal ROIs were manually contoured. Radiomics features (n = 153) were used to train a radial basis function support vector machine classifier to discriminate between viable GCT/mature teratoma versus fibrosis. A nested 10-fold cross-validation protocol was used to determine classifier accuracy. Clinical variables/restricted size criteria were used to optimize the classifier. Results Seventy-seven patients with 102 ROIs were analyzed (GCT, 21; teratoma, 41; fibrosis, 40). The discriminative accuracy of radiomics to identify GCT/teratoma versus fibrosis was 72 ± 2.2% (area under the curve [AUC], 0.74 ± 0.028); sensitivity was 56.2 ± 15.0%, and specificity was 81.9 ± 9.0% ( P = .001). No major predictive differences were identified when data were restricted by varying maximal axial diameters (AUC range, 0.58 ± 0.05 to 0.74 ± 0.03). The prediction algorithm using clinical variables alone identified an AUC of 0.76. When these variables were added to the radiomics signature, the best performing classifier was identified when axial masses were limited to diameter < 2 cm (accuracy, 88.2 ± 4.4; AUC, 0.80 ± 0.05; P = .02). Conclusion A predictive radiomics algorithm had a discriminative accuracy of 72% that improved to 88% when combined with clinical predictors. Additional independent validation is required to assess whether radiomics allows patients with a high predicted likelihood of fibrosis to avoid RPLND.

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Bounhas, Ibrahim, Raja Ayed, Bilel Elayeb, Fabrice Evrard, and Narjès Bellamine Ben Saoud. "Experimenting a discriminative possibilistic classifier with reweighting model for Arabic morphological disambiguation." Computer Speech & Language 33, no. 1 (September 2015): 67–87. http://dx.doi.org/10.1016/j.csl.2014.12.005.

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Ropelewska, Ewa, Wioletta Popińska, Kadir Sabanci, and Muhammet Fatih Aslan. "Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision." European Food Research and Technology 248, no. 3 (December 23, 2021): 893–98. http://dx.doi.org/10.1007/s00217-021-03935-3.

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AbstractThe aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

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Wan, Fang, Tianning Yuan, Mengying Fu, Xiangyang Ji, Qingming Huang, and Qixiang Ye. "Nearest Neighbor Classifier Embedded Network for Active Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 10041–48. http://dx.doi.org/10.1609/aaai.v35i11.17205.

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Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability of the discriminative classifier (the softmax classifier) is questionable when there is a significant distribution bias between the labeled set and the unlabeled set. In this paper, we attempt to replace the softmax classifier in deep neural network with a nearest neighbor classifier, considering its progressive generalization ability within the unknown sub-space. Our proposed active learning approach, termed nearest Neighbor Classifier Embedded network (NCE-Net), targets at reducing the risk of over-estimating unlabeled samples while improving the opportunity to query informative samples. NCE-Net is conceptually simple but surprisingly powerful, as justified from the perspective of the subset information, which defines a metric to quantify model generalization ability in active learning. Experimental results show that, with simple selection based on rejection or confusion confidence, NCE-Net improves state-of-the-arts on image classification and object detection tasks with significant margins.

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Lu, Cheng, Chuangao Tang, Jiacheng Zhang, and Yuan Zong. "Progressively Discriminative Transfer Network for Cross-Corpus Speech Emotion Recognition." Entropy 24, no. 8 (July 29, 2022): 1046. http://dx.doi.org/10.3390/e24081046.

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Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space. In this case, excessively narrowing the domain distance will impair the emotion discrimination of speech features since it is difficult to maintain the completeness of the emotion space only by an emotion classifier. To overcome this issue, we propose a progressively discriminative transfer network (PDTN) for cross-corpus SER in this paper, which can enhance the emotion discrimination ability of speech features while eliminating the mismatch between the source and target corpora. In detail, we design two special losses in the feature layers of PDTN, i.e., emotion discriminant loss Ld and distribution alignment loss La. By incorporating prior knowledge of speech emotion into feature learning (i.e., high and low valence speech emotion features have their respective cluster centers), we integrate a valence-aware center loss Lv and an emotion-aware center loss Lc as the Ld to guarantee the discriminative learning of speech emotions except an emotion classifier. Furthermore, a multi-layer distribution alignment loss La is adopted to more precisely eliminate the discrepancy of feature distributions between the source and target domains. Finally, through the optimization of PDTN by combining three losses, i.e., cross-entropy loss Le, Ld, and La, we can gradually eliminate the domain mismatch between the source and target corpora while maintaining the emotion discrimination of speech features. Extensive experimental results of six cross-corpus tasks on three datasets, i.e., Emo-DB, eNTERFACE, and CASIA, reveal that our proposed PDTN outperforms the state-of-the-art methods.

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Bhuvaneswari, M. "Gaussian mixture model: An application to parameter estimation and medical image classification." Journal of Scientific and Innovative Research 5, no. 3 (June 25, 2016): 100–105. http://dx.doi.org/10.31254/jsir.2016.5308.

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Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques. The performance of the classification strategy of both the classifiers used has a better proficiency when compared to the other classifiers. By combining the SVM and GMM we co uld be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques. In this the GMM classifier and the SVM classifier are trained usin g the parameters and they are to be compared.

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Wang, Yuhang, Tao Feng, and Zheng Yi. "Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics." Computer Science and Information Systems, no. 00 (2022): 42. http://dx.doi.org/10.2298/csis210322042w.

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Using deep map sequence to recognize human action is an important research field in computer vision. The traditional deep map-based methods have a lot of redundant information. Therefore, this paper proposes a new deep map sequence feature expression method based on discriminative collaborative representation classifier, which highlights the time sequence of human action features. In this paper, the energy field is established according to the shape and action characteristics of human body to obtain the energy information of human body. Then the energy information is projected onto three orthogonal axes to obtain deep spatial temporal energy map. Meanwhile, in order to solve the problem of high misclassification probability of similar samples by collaborative representation classifier (CRC), a discriminative CRC (DCRC) is proposed. The classifier takes into account the influence of all training samples and each kind of samples on the collaborative representation coefficient, it obtains the highly discriminative collaborative representation coefficient, and improves the discriminability of similar samples. Experimental results on MSR Action3D data set show that the redundancy of key-frame algorithm is reduced, and the operation efficiency of each algorithm is improved by 20%-30%. The proposed algorithm in this paper reduces the redundant information in deep map sequence and improves the extraction rate of feature map. It not only preserves the spatial information of human action through the energy field, but also records the temporal information of human action in a complete way. What?s more, it still maintains a high recognition accuracy in the action data with temporal information.

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Kumar Bhowmik, Tapan. "Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation." Inteligencia Artificial 18, no. 56 (December 18, 2015): 14. http://dx.doi.org/10.4114/intartif.vol18iss56pp14-30.

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This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments, it is observed that LR learning with gradient ascent technique outperforms general NB classifier. However, under Gaussian Naive Bayes assumption, both classifiers NB and LR perform similar.

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Lewin, Jeremy Howard, Paul Dufort, Jaydeep Halankar, Martin O'Malley, Michael A. S. Jewett, Robert James Hamilton, Abha A. Gupta, et al. "Applying radiomics to predict pathology of post chemotherapy retroperitoneal nodal masses in germ cell tumors (GCT)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 4559. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.4559.

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4559 Background: After chemotherapy, > 50% of patients (pts) with metastatic testicular GCT who undergo retroperitoneal lymph node dissection (RPNLD) for residual masses are found to have fibrosis (F) alone on pathological examination. To minimize overtreatment, better prediction algorithms are needed to identify pts with F who can avoid RPLND. Radiomics uses image processing techniques to extract quantitative textures/features from tumor regions of interest (ROI) to train a classifier that predicts pathological findings. We hypothesized that radiomics may identify pts with a high predicted likelihood of F who may avoid RPLND. Methods: Pts with GCT who had an RPLND for nodal masses > 1cm after first line platinum chemotherapy were included. Preoperative contrast enhanced axial CT images of retroperitoneal ROI were manually contoured. 153 radiomics features trained a radial basis function support vector machine classifier to discriminate between viable GCT /Mature Teratoma (T) vs F. Nested ten-fold cross-validation protocol was employed to determine classifier accuracy. Clinical variables and restricted size criteria were used to optimize the classifier. Results: A total of 82 pts with 102 ROI were analyzed (GCT: 21; T: 41; F: 40). The discriminative accuracy of radiomics to identify GCT/T vs F was 72%(±2.2)(AUC: 0.74 (±0.028); positive predictive value: 67% (48-92%); negative predictive value: 74% (62-84%)(p = 0.001)). No major predictive differences were identified when data was restricted by varying maximal axial diameters (AUC range: 0.58(±0.05) - 0.74(±0.03)). Prediction algorithm using clinical variables alone identified an AUC of 0.71 (±0.15). When these variables were added to the radiomic signature, the best performing classifier was identified when axial tumors were limited to diameter < 2cm (accuracy: 88.2 (±4.4); AUC: 0.80 (±0.05)(p = 0.02)). Conclusions: A predictive radiomics algorithm had an overall discriminative accuracy of 72% that improved to 88% when combined with clinical details. Further independent validation is required to assess whether radiomics, in conjunction with standard clinical predictors, may allow pts with a high predicted likelihood of F to avoid RPLND.

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Liang, Jinglian, Chao Xu, Zhiyong Feng, and Xirong Ma. "Hidden Markov Model Decision Forest for Dynamic Facial Expression Recognition." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 07 (September 28, 2015): 1556010. http://dx.doi.org/10.1142/s0218001415560108.

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Facial expressions can be mainly conveyed by only a few discriminative facial regions of interest. In this paper, we study the discriminative regions for facial expression recognition from video sequences. The goal of our method is to explore and make use of the discriminative regions for different facial expressions. For this purpose, we propose a Hidden Markov Model (HMM) Decision Forest (HMMDF). In this framework, each tree node is a discriminative classifier, which is constructed by combining weighted HMMs. Motivated by a psychological theory of "elimination by aspects", several HMMs on each node are modeled respectively for facial regions, which have discriminative capabilities for classification. The weights for these HMMs can be further adjusted according to the contributions of facial regions. Extensive experiments validate the effectiveness of discriminative regions on facial expression, and the experimental results show that the proposed HMMDF framework yields dramatic improvements in facial expression recognition compared to existing methods.

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Paranjape, Parnika N., Meera M. Dhabu, and Parag S. Deshpande. "dSubSign: Classification of Instance-Feature Data Using Discriminative Subgraphs as Class Signatures." International Journal of Software Engineering and Knowledge Engineering 31, no. 07 (July 2021): 917–47. http://dx.doi.org/10.1142/s0218194021500285.

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Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods.

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Journal articles on the topic 'Discriminative classifier'

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