Статті в журналах: "Classification probabiliste"

1

Mselati, Benoit. "Classification et représentation probabiliste des solutions positives d'une équation elliptique semi-linéaire." Comptes Rendus Mathematique 335, no. 9 (November 2002): 733–38. http://dx.doi.org/10.1016/s1631-073x(02)02557-8.

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2

Garbolino, Emmanuel, Patrice De Ruffray, Henry Brisse, and Gilles Grandjouan. "Les phytoclimats de France : classification probabiliste de 1874 bio-indicateurs du climat." Comptes Rendus Biologies 331, no. 11 (November 2008): 881–95. http://dx.doi.org/10.1016/j.crvi.2008.08.009.

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3

Stan, Emanuela, Camelia-Oana Muresan, Raluca Dumache, Veronica Ciocan, Stefania Ungureanu, Dan Costachescu, and Alexandra Enache. "Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population." Applied Sciences 14, no. 10 (May 13, 2024): 4136. http://dx.doi.org/10.3390/app14104136.

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This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, only one misclassification occurred. Regarding accuracy, the overall rate remained notably high at 98.24%. All female specimens that could be estimated were correctly classified (100% accuracy), while for males, the accuracy rate was 95.65%. Undetermined cases were noted to potentially impact the accuracy of sex classification, underscoring the critical role of precision in forensic contexts. In conclusion, the study highlights the importance of accuracy in forensic sex estimation. It emphasizes the confidence with which DSP software can be utilized, if not the only method, at least as a preliminary or adjuvantly accurate technique for sex estimation in forensic anthropology.

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4

Gamboa, Luis Fernando. "Strategic Uses of Mobile Phones in the BoP: Some Examples in Latin American Countries." Lecturas de Economía, no. 71 (February 23, 2010): 209–34. http://dx.doi.org/10.17533/udea.le.n71a4820.

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El objetivo del trabajo es analizar el uso de un conjunto de estrategias para minimizar el gasto en telefonía móvil en una encuesta de telefonía móvil para personas de bajos ingresos en Argentina, Brasil, Colombia, México y Perú. La metodología empleada incluye dos etapas; primero, se evalúa cuáles son los determinantes del uso de cada estrategia mediante un modelo probabilístico y se encuentra que la edad y el nivel de escolaridad influyen positivamente en la probabilidad de usar las alternativas; segundo, se utiliza un modelo de Poisson para evaluar el número de estrategias utilizadas. Aunque los resultados difieren entre países, es común encontrar que los usuarios tienden a utilizar varias estrategias. Palabras clave: Telefonía móvil, pobreza, modelos de conteo. Clasificación JEL : D12, C35, L86 Abstrac: This paper studies the determinants of the use of different strategies by mobile-users for reducing their spending. This empirical exercise is done with a special survey focused in lowincome people from developing countries such as Argentina, Brazil, Colombia, Mexico, and Peru. Our methodology is the following. First, we evaluate the determinants of use of each strategy by means of a probabilistic model and we find that education level and age are important determinants of the use of alternatives. Second, we use a Poisson regression model to study the number of strategies used. Although our findings differ among countries, the use of more than one strategy is common in the sample. Keywords: Mobile Phones, Poverty, Count Data. JEL Classification: D12, C35, L86 Résumé: L'objectif de cet article est d'analyser l'utilisation d'un ensemble de stratégies visant diminuer les dépenses dans l'utilisation des téléphones portables, à partir d'un sondage fait chez les personnes à bas revenu en Argentine, Brésil, Colombie, Mexique et Pérou. La méthodologie employée considère deux étapes: Premièrement, il s'agit de déterminer les causes de l'utilisation de chaque stratégie à travers un modèle probabiliste, ce qui nous a permis de conclure que l'âge et leniveau de scolarité des personnes ont un impact positif sur la probabilité d'utiliser les stratégies. Deuxièmement, on utilise un modèle Poisson pour évaluer le nombre de stratégies utilisées. Même si les résultats diffèrent entre les pays considérés, nous trouvons que les usagers des portables ont une tendance à utiliser plusieurs stratégies. Mots clé: Téléphonie mobile, pauvreté, modèles de comptage. Classification JEL : D12, C35, L86

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5

Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (December 27, 2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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6

Selianinau, Mikhail. "Podejście probabilistyczne do klasyfikacji cyfrowych obrazów twarzy." Prace Naukowe Akademii im. Jana Długosza w Częstochowie. Technika, Informatyka, Inżynieria Bezpieczeństwa 6 (2018): 563–74. http://dx.doi.org/10.16926/tiib.2018.06.40.

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7

Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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8

Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.

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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.

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9

Villa, Joe Luis, Ricard Boqué, and Joan Ferré. "Calculation of the probability of correct classification in probabilistic bagged k-Nearest Neighbours." Chemometrics and Intelligent Laboratory Systems 94, no. 1 (November 2008): 51–59. http://dx.doi.org/10.1016/j.chemolab.2008.06.007.

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10

Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.

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11

DASHEVSKIY, MIKHAIL, and ZHIYUAN LUO. "RELIABLE PROBABILISTIC CLASSIFICATION OF INTERNET TRAFFIC." International Journal of Information Acquisition 06, no. 02 (June 2009): 133–46. http://dx.doi.org/10.1142/s0219878909001837.

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Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.

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12

Zhao, Yue, Ye Yuan, and Guoren Wang. "Keyword Search over Probabilistic XML Documents Based on Node Classification." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/210961.

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This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

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13

Ribeiro, Rodrigo Otávio de Araújo, Lidia Angulo Meza, and Annibal Parracho Sant'Anna. "Probabilistic Preferences Composition in the Classification of Apparel Retail Stores." International Journal of Business Analytics 2, no. 4 (October 2015): 64–78. http://dx.doi.org/10.4018/ijban.2015100104.

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This paper employs the probabilistic composition of preferences to classify stores by their operational efficiency. Probabilistic composition of preferences is a multicriteria analysis methodology based on the transformation of assessments by multiple attributes into probabilities of choice. The numerical initial measurements provide estimates for location parameters of probability distributions that are compared to measure the preferences. The probabilities of choice according to each attribute separately are aggregated according to probabilistic composition rules. A classification of two sets of stores into five classes is performed.

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14

Huanhuan Chen, P. Tino, and Xin Yao. "Probabilistic Classification Vector Machines." IEEE Transactions on Neural Networks 20, no. 6 (June 2009): 901–14. http://dx.doi.org/10.1109/tnn.2009.2014161.

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15

Fava, Bruno, Paulo C. Marques F., and Hedibert F. Lopes. "Probabilistic Nearest Neighbors Classification." Entropy 26, no. 1 (December 30, 2023): 39. http://dx.doi.org/10.3390/e26010039.

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Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.

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16

Ke, Wen-Chyan. "The Sensitivity to Trade Classification Algorithms for Estimating the Probability of Informed Trading." International Journal of Trade, Economics and Finance 5, no. 5 (October 2014): 392–96. http://dx.doi.org/10.7763/ijtef.2014.v5.404.

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17

Haberman, Shelby J. "PROBABILITY PREDICTION AND CLASSIFICATION." ETS Research Report Series 2004, no. 1 (June 2004): i—23. http://dx.doi.org/10.1002/j.2333-8504.2004.tb01946.x.

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18

Pernin, Jérôme, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin. "Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 2 (October 12, 2016): 115–36. http://dx.doi.org/10.5194/ascmo-2-115-2016.

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Abstract. Air mass classification has become an important area in synoptic climatology, simplifying the complexity of the atmosphere by dividing the atmosphere into discrete similar thermodynamic patterns. However, the constant growth of atmospheric databases in both size and complexity implies the need to develop new adaptive classifications. Here, we propose a robust unsupervised and supervised classification methodology of a large thermodynamic dataset, on a global scale and over several years, into discrete air mass groups homogeneous in both temperature and humidity that also provides underlying probability laws. Temperature and humidity at different pressure levels are aggregated into a set of cumulative distribution function (CDF) values instead of classical ones. The method is based on a Gaussian mixture model and uses the expectation–maximization (EM) algorithm to estimate the parameters of the mixture. Spatially gridded thermodynamic profiles come from ECMWF reanalyses spanning the period 2000–2009. Different aspects are investigated, such as the sensitivity of the classification process to both temporal and spatial samplings of the training dataset. Comparisons of the classifications made either by the EM algorithm or by the widely used k-means algorithm show that the former can be viewed as a generalization of the latter. Moreover, the EM algorithm delivers, for each observation, the probabilities of belonging to each class, as well as the associated uncertainty. Finally, a decision tree is proposed as a tool for interpreting the different classes, highlighting the relative importance of temperature and humidity in the classification process.

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19

Wazarkar, Seema, Bettahally N. Keshavamurthy, and Ahsan Hussain. "Probabilistic Classifier for Fashion Image Grouping Using Multi-Layer Feature Extraction Model." International Journal of Web Services Research 15, no. 2 (April 2018): 89–104. http://dx.doi.org/10.4018/ijwsr.2018040105.

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In this article, probabilistic classification model is designed for the fashion-related images collected from social networks. The proposed model is divided into two parts. The first is feature extraction where six important features are taken into consideration to deal with heterogeneous nature of the given images. The second classification is done with the help of probability computations to get collection of homogeneous images. Here, class-conditional probability of extracted features are calculated, then joint probability is used for the classification. Class label with maximum joint probability is assigned to the given image. A comparative study of proposed classification model with existing popular supervised as well as unsupervised classification approaches is done on the basis of obtained accuracy of the results. The effect of convolutional neural network inclusion in the proposed feature extraction model is also shown where it improves the accuracy of final results. The output of this system is useful further for fashion trend analysis.

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20

Jia, Li, Hai Yan, Guo Hui Li, and Hui Zhang. "Target Classification Using PAS and Evidence Theory." Applied Mechanics and Materials 347-350 (August 2013): 3728–33. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3728.

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This paper presents a novel Dempster-Shafer evidence construction approach for aircraft aim recognition. The prior-probability of the properties of aircraft was used for establishing a probabilistic argumentation system. Dempster-Shafer evidence was constructed by assumption-based reasoning. Therefore, additional information could be provided to the classification of the data fusion system. Experiments on artificial and real data demonstrated that the proposed method could improve the classification results.

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21

Khan, Khalil, Muhammad Attique, Ikram Syed, and Asma Gul. "Automatic Gender Classification through Face Segmentation." Symmetry 11, no. 6 (June 6, 2019): 770. http://dx.doi.org/10.3390/sym11060770.

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Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).

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22

Duarte-Mermoud, M. A., N. H. Beltrán, and S. A. Salah. "Probabilistic Adaptive Crossover Applied to Chilean Wine Classification." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/734151.

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Recently, a new crossover technique for genetic algorithms has been proposed. The technique, called probabilistic adaptive crossover (PAX), includes the estimation of the probability distribution of the population, storing the information regarding the best and the worst solutions of the problem being solved in a probability vector. The use of the proposed technique to face Chilean wine classification based on chromatograms obtained from an HPLC is reported in this paper. PAX is used in the first stage as the feature selection method and then support vector machines (SVM) and linear discriminant analysis (LDA) are used as classifiers. The results are compared with those obtained using the uniform (discrete) crossover standard technique and a variant of PAX called mixed crossover.

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23

Lee, Youngjae, and Hyeyoung Park. "Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification." Applied Sciences 11, no. 22 (November 19, 2021): 10977. http://dx.doi.org/10.3390/app112210977.

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In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.

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24

FERSINI, ELISABETTA, and ENZA MESSINA. "WEB PAGE CLASSIFICATION THROUGH PROBABILISTIC RELATIONAL MODELS." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 04 (June 2013): 1350013. http://dx.doi.org/10.1142/s0218001413500134.

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In the last decade, new approaches focused on modeling uncertainty over complex relational data have been developed. In this paper, one of the most promising of such approaches, known as probabilistic relational model (PRM), has been investigated and extended in order to measure and include semantic relationships for addressing web page classification problems. Experimental results show the potential of the proposed method of capturing the "strength" of existing relationships (links) and the capacity of including this information into the probability model.

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25

She, Qingshan, Yuliang Ma, Ming Meng, and Zhizeng Luo. "Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/251945.

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Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

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26

Amirgaliyev, Yedilkhan, Vladimir Berikov, Lyailya Cherikbayeva, Konstantin Latuta, and Kalybekuuly Bekturgan. "Group approach to solving the tasks of recognition." Yugoslav Journal of Operations Research 29, no. 2 (2019): 177–92. http://dx.doi.org/10.2298/yjor180822032y.

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In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

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Ech-Chelfi, Wiame, and Hammoumi El. "Survey on the relation between road freight transport, SCM and sustainable development." Yugoslav Journal of Operations Research 29, no. 2 (2019): 151–76. http://dx.doi.org/10.2298/yjor180915006e.

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In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. A probabilistic model of classification with the use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

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Andrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (October 1, 2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.

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Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertainty calibration. Upon investigating the effect of training with imbalanced dataset on the models, results show that each model performance decreases with an increasing number of the majority class in the training set. Interestingly, with a number of negative class 10× that of positive class, the models still provide reasonably well calibrated uncertainty, i.e. an expected Uncertainty Calibration Error (UCE) less than 6%. We also show in this study how, in the case of relatively small amount of training dataset, a convolutional neural network based classifier trained via Bayesian Active Learning by Disagreement (BALD) performs. We find that, with an optimized number of training examples, the model — being the most confident in its predictions — generalizes relatively well and produces the best uncertainty calibration which corresponds to UCE = 3.118%.

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Knowlton, B. J., L. R. Squire, and M. A. Gluck. "Probabilistic classification learning in amnesia." Learning & Memory 1, no. 2 (1994): 106–20. http://dx.doi.org/10.1101/lm.1.2.106.

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Amnesic patients and control subjects participated in a study of probabilistic classification learning. In each of three tasks, four different cues were each probabilistically associated with one of two outcomes. On each trial, the cues could appear alone or in combination with other cues and subjects selected the outcome they thought was correct. Feedback was provided after each trial. In each task, the amnesic patients learned gradually to associate the cues with the appropriate outcome at the same rate as control subjects, improving from 50% correct to approximately 65% correct. Presumably because the cue-outcome associations were probabilistic, declarative memory for the outcomes of specific trials was not as useful for performance as the information gradually accrued across trials. Nevertheless, declarative memory does appear to make a contribution to performance when training is extended beyond approximately 50 trials, because with further training control subjects eventually outperformed the amnesic patients. It was also demonstrated that performance on the probabilistic classification task was not the result of holding knowledge of cue-outcome associations in short-term memory, because both control subjects and amnesic patients demonstrated significant savings when testing was interrupted by a 5-min delay (experiment 2). Probabilistic classification learning appears to provide an analog in human subjects for the habit learning tasks that can be acquired normally by animals with hippocampal lesions.

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30

Di Mauro, Nicola, Claudio Taranto, and Floriana Esposito. "Link classification with probabilistic graphs." Journal of Intelligent Information Systems 42, no. 2 (January 15, 2014): 181–206. http://dx.doi.org/10.1007/s10844-013-0293-0.

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31

Lyu, Shengfei, Xing Tian, Yang Li, Bingbing Jiang, and Huanhuan Chen. "Multiclass Probabilistic Classification Vector Machine." IEEE Transactions on Neural Networks and Learning Systems 31, no. 10 (October 2020): 3906–19. http://dx.doi.org/10.1109/tnnls.2019.2947309.

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32

Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, this is referred to as predictive multiplicity i.e. the prevalence of conflicting predictions assigned by near-optimal competing models. In this paper, we present a framework for measuring predictive multiplicity in probabilistic classification (predicting the probability of a positive outcome). We introduce measures that capture the variation in risk estimates over the set of competing models, and develop optimization-based methods to compute these measures efficiently and reliably for convex empirical risk minimization problems. We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks. Further, we provide insight into how predictive multiplicity arises by analyzing the relationship between predictive multiplicity and data set characteristics (outliers, separability, and majority-minority structure). Our results emphasize the need to report predictive multiplicity more widely.

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33

Lewicki, Michael S. "Bayesian Modeling and Classification of Neural Signals." Neural Computation 6, no. 5 (September 1994): 1005–30. http://dx.doi.org/10.1162/neco.1994.6.5.1005.

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Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these problems is obtained by applying Bayesian probability theory. By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified. In addition, this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.

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34

Andreev, O. A., and A. T. Trofimov. "SYNTHESIS OF NEURAL NETWORK ALGORITHMS FOR CLASSIFICATION OF MARINE OBJECTS IN LOW-FREQUENCY PASSIVE SONAR SYSTEMS." Bulletin of Dubna International University for Nature, Society, and Man. Series: Natural and engineering sciences, no. 3 (44) (December 27, 2019): 3–8. http://dx.doi.org/10.37005/1818-0744-2019-3-3-8.

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The paper addresses the issue of insuring the required probability of correct classification of marine objects in low-frequency passive sonar systems. The solution to the issue is sought through the application of methods for the synthesis of neural network classification algorithms using poly-Gaussian probabilistic models (Gaussian mixture models, GMM). It is shown that the use of GMM makes it possible to solve a number of problems specific to the issue; classification algorithms synthesized using mentioned methods can be implemented in the form of neural networks, which in turn can be described in C++/VHDL to create endpoint computing devices or software systems. The results of modeling of synthesized classification algorithms on experimental data are presented; it is demonstrated that such algorithms make it possible to increase the probability of correct classification of marine objects and to satisfy typical requirements for classification systems in low-frequency passive sonar systems.

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35

Hartigan, J. A. "Introduction: Classification, probability and statistics." Computational Statistics & Data Analysis 23, no. 1 (November 1996): 3–4. http://dx.doi.org/10.1016/s0167-9473(96)00017-5.

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36

Babkov, Yu V., E. E. Belova, and M. I. Potapov. "On the classification of motive power failures." Dependability 21, no. 4 (December 27, 2021): 12–19. http://dx.doi.org/10.21683/1729-2646-2021-21-4-12-19.

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The Aim of the article is to develop a motive power failure classification to enable substantiated definition of dependability requirements for motive power as a part of a railway transportation system, as well as for organizing systematic measures to ensure a required level of its dependability over the life cycle. Methods. The terminology of interstate dependability-related standards was analysed and the two classifications used by OJSC “RZD” for estimating the dependability of technical systems and motive power were compared. The dependability of railway transportation systems is studied using structural and logical and logical and probabilistic methods of dependability analysis, while railway lines are examined using the graph theory and the Markov chains. Results. An analysis of the existing failure classifications identified shortcomings that prevent the use of such classifications for studying the structural dependability of such railway transportation systems as motive power. A classification was developed that combines two failure classifications (“category-based” for the transportation process and technical systems and “type-based” for the motive power), but this time with new definitions. The proposed classification of the types of failures involves stricter definitions of the conditions and assumptions required for evaluating the dependability and technical condition of an item, which ensures correlation between the characteristics of motive power and its dependability throughout the life cycle in the context of the above tasks. The two classifications could be used simultaneously while researching structural problems of dependability using logical and probabilistic methods and Markov chains. The developed classification is included in the provisions of the draft interstate standard “Dependability of motive power. Procedure for the definition, calculation methods and supervision of dependability indicators throughout the life cycle” that is being prepared by JSC “VNIKTI” in accordance with the OJSC “RZD” research and development plan. Conclusion. The article’s findings will be useful to experts involved in the evaluation of motive power dependability.

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37

Wagh, Trupti, Jolnar Assi, and Ammar H. Mohammed. "Probability Graphical Model for Predicting Probability of Default For Mortgage Loans." International Journal of Data Science and Advanced Analytics 5, no. 5 (November 13, 2023): 244–50. http://dx.doi.org/10.69511/ijdsaa.v5i5.210.

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Assessment of Default risk of borrowers is important for lending institutions as it directly affects profits and losses of the firm and guides in compensating the risk by taking appropriate majors for loans having higher probability of default. Predicting probability of default using statistical and machine learning models has been a popular research topic in data science community. While different types of classification models have been proposed historically, there is scope to apply probabilistic inference to the mortgage default analysis to support decision making. Probabilistic Graphical Model (PGM) are a powerful framework for compactly encoding probability distributions over complex multivariate domains using graphical representations. Due to the high interpretability and inherent support for probabilistic inference, the PGM models have widely been used under various domains such as medical diagnosis, text, audio, video processing.

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38

张, 俊达. "Volume Data Classification Visualization Based on Probabilistic Classification Model." Computer Science and Application 09, no. 11 (2019): 1986–92. http://dx.doi.org/10.12677/csa.2019.911223.

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39

SIVALINGAM, DAYAN MANOHAR, NARENKUMAR PANDIAN, and JEZEKIEL BEN-ARIE. "MINIMAL CLASSIFICATION METHOD WITH ERROR-CORRECTING CODES FOR MULTICLASS RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 05 (August 2005): 663–80. http://dx.doi.org/10.1142/s0218001405004241.

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In this work, we develop an efficient technique to transform a multiclass recognition problem into a minimal binary classification problem using the Minimal Classification Method (MCM). The MCM requires only log 2 N classifications whereas the other methods require much more. For the classification, we use Support Vector Machine (SVM) based binary classifiers since they have superior generalization performance. Unlike the prevalent one-versus-one strategy (the bottom-up one-versus-one strategy is called tournament method) that separates only two classes at each classification, the binary classifiers in our method have to separate two groups of multiple classes. As a result, the probability of generalization error increases. This problem is alleviated by utilizing error correcting codes, which results only in a marginal increase in the required number of classifications. However, in comparison to the tournament method, our method requires only 50% of the classifications and still similar performance can be attained. The proposed solution is tested with the Columbia Object Image Library (COIL). We also test the performance under conditions of noise and occlusion.

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40

Heese, Raoul, Patricia Bickert, and Astrid Elisa Niederle. "Representation of binary classification trees with binary features by quantum circuits." Quantum 6 (March 30, 2022): 676. http://dx.doi.org/10.22331/q-2022-03-30-676.

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We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.

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41

Liu, Zhege, Junxing Cao, Yujia Lu, Shuna Chen, and Jianli Liu. "A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification." Interpretation 7, no. 3 (August 1, 2019): SE225—SE236. http://dx.doi.org/10.1190/int-2018-0238.1.

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In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.

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42

von Specht, Sebastian, and Fabrice Cotton. "A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification." Bulletin of the Seismological Society of America 110, no. 6 (July 14, 2020): 2777–800. http://dx.doi.org/10.1785/0120190133.

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ABSTRACT The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation–maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastías and Montalva (2016) with ∼2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.

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Zheng, Wei, and Feng Qian. "Promptly assessing probability of barge–bridge collision damage of piers through probabilistic-based classification of machine learning." Journal of Civil Structural Health Monitoring 7, no. 1 (February 2017): 57–78. http://dx.doi.org/10.1007/s13349-017-0208-9.

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44

Niyomugabo, Cesar, Hyo-rim Choi, and Tae Yong Kim. "A Modified Adaboost Algorithm to Reduce False Positives in Face Detection." Mathematical Problems in Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/5289413.

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We present a modified Adaboost algorithm in face detection, which aims at an accurate algorithm to reduce false-positive detection rates. We built a new Adaboost weighting system that considers the total error of weak classifiers and classification probability. The probability was determined by computing both positive and negative classification errors for each weak classifier. The new weighting system gives higher weights to weak classifiers with the best positive classifications, which reduces false positives during detection. Experimental results reveal that the original Adaboost and the proposed method have comparable face detection rate performances, and the false-positive results were reduced almost four times using the proposed method.

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45

Arnason, R. M., P. Barmby, and N. Vulic. "Identifying new X-ray binary candidates in M31 using random forest classification." Monthly Notices of the Royal Astronomical Society 492, no. 4 (February 3, 2020): 5075–88. http://dx.doi.org/10.1093/mnras/staa207.

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ABSTRACT Identifying X-ray binary (XRB) candidates in nearby galaxies requires distinguishing them from possible contaminants including foreground stars and background active galactic nuclei. This work investigates the use of supervised machine learning algorithms to identify high-probability XRB candidates. Using a catalogue of 943 Chandra X-ray sources in the Andromeda galaxy, we trained and tested several classification algorithms using the X-ray properties of 163 sources with previously known types. Amongst the algorithms tested, we find that random forest classifiers give the best performance and work better in a binary classification (XRB/non-XRB) context compared to the use of multiple classes. Evaluating our method by comparing with classifications from visible-light and hard X-ray observations as part of the Panchromatic Hubble Andromeda Treasury, we find compatibility at the 90 per cent level, although we caution that the number of source in common is rather small. The estimated probability that an object is an XRB agrees well between the random forest binary and multiclass approaches and we find that the classifications with the highest confidence are in the XRB class. The most discriminating X-ray bands for classification are the 1.7–2.8, 0.5–1.0, 2.0–4.0, and 2.0–7.0 keV photon flux ratios. Of the 780 unclassified sources in the Andromeda catalogue, we identify 16 new high-probability XRB candidates and tabulate their properties for follow-up.

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46

Le, Phuong Bich, and Zung Tien Nguyen. "ROC Curves, Loss Functions, and Distorted Probabilities in Binary Classification." Mathematics 10, no. 9 (April 22, 2022): 1410. http://dx.doi.org/10.3390/math10091410.

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The main purpose of this work is to study how loss functions in machine learning influence the “binary machines”, i.e., probabilistic AI models for predicting binary classification problems. In particular, we show the following results: (i) Different measures of accuracy such as area under the curve (AUC) of the ROC curve, the maximal balanced accuracy, and the maximally weighted accuracy are topologically equivalent, with natural inequalities relating them; (ii) the so-called real probability machines with respect to given information spaces are the optimal machines, i.e., they have the highest precision among all possible machines, and moreover, their ROC curves are automatically convex; (iii) the cross-entropy and the square loss are the most natural loss functions in the sense that the real probability machine is their minimizer; (iv) an arbitrary strictly convex loss function will also have as its minimizer an optimal machine, which is related to the real probability machine by just a reparametrization of sigmoid values; however, if the loss function is not convex, then its minimizer is not an optimal machine, and strange phenomena may happen.

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47

Liao, Xiaofeng, Bo Li, and Bo Yang. "A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis." Computational Intelligence and Neuroscience 2018 (November 5, 2018): 1–15. http://dx.doi.org/10.1155/2018/1458962.

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The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward’s clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering validity indexes are used to determine the range of the optimal number of clusters. In order to narrow this scope and find the optimal number of classifications, a sufficient number of samples are selected in the vicinity of each class center to train probabilistic neural networks, which correspond to different number of classifications. Then, the classifier of the optimal probabilistic neural network is obtained by calculating the maximum value of classification validity index. Finally, the identification accuracy of the classifier is improved effectively by using the method of Bivariable correlation analysis. Simulation results also illustrate that the proposed algorithms can accurately identify the pulse emitter signals.

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48

Li, Sheng, Changhong Duan, Yuan Gao, and Yuhao Cai. "Classification Study of New Power System Stability Considering Stochastic Disturbance Factors." Sustainability 15, no. 24 (December 6, 2023): 16614. http://dx.doi.org/10.3390/su152416614.

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Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda. However, with a large increase in stochastic disturbance factors (SDFs), the system gradually shows strong stochasticity, and the stability presents greater complexity. It is necessary to analyze the impact on the system based on different processing methods of SDFs to maintain system stability. This paper delves into the impact of SDFs on system stability by analyzing and summarizing both stochastic variables and processes. Initially, the SDFs in the power system are meticulously analyzed and categorized. When the SDFs are treated as stochastic variables, the probabilistic stability is classified and evaluated based on a probability analysis method, which includes the probabilistic small-disturbance stability, the probabilistic transient stability, and the probabilistic voltage stability. When the SDFs are treated as stochastic processes, the stochastic stability is classified and evaluated by using a stochastic analysis method, including the stochastic small-disturbance stability, the stochastic transient stability, and the stochastic voltage stability. Finally, the research perspectives on SDFs and system stability are discussed and prospected.

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49

Siregar, Siti Julianita, Ahmadi Irmansyah Lubis, and Erika Fahmi Ginting. "Penerapan Neural Network Dalam Klasifikasi Citra Permainan Batu Kertas Gunting dengan Probabilistic Neural Network." Building of Informatics, Technology and Science (BITS) 3, no. 3 (December 31, 2021): 420–25. http://dx.doi.org/10.47065/bits.v3i3.1143.

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In this research, an image classification model was developed to distinguish hand objects pointing at rock, paper, and scissors using one of the popular image classification methods, namely the Probabilistic Neural Network. Probabilistic Neural Network is a method in an artificial neural network that is used to classify a category based on the results of calculating the distance between the density function and the probability. PNN has 4 stages of processing, namely Input Layer, Pattern Layer, Summation Layer, and Output Layer. Tests in the study were carried out with a total of 60 testing data from three object classes from the dataset. Then the results of the classification of Batu, Scissors, and Paper hand images using the application of the PNN algorithm in this research test obtained an average accuracy value of 90%

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Manna, Subhankar, and Malathi G. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research -GRANTHAALAYAH 5, no. 8 (August 31, 2017): 260–66. http://dx.doi.org/10.29121/granthaalayah.v5.i8.2017.2229.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using R package. This work used the dataset that is imported from the UCI Machine Learning Repository, Diabetes 130-US hospitals for years 1999-2008 Data Set. Motivation/Background: Naïve Bayes is a probabilistic classifier based on Bayes theorem. It provides useful perception for understanding many algorithms. In this paper when Bayesian algorithm applied on diabetes dataset, it shows high accuracy. Is assumes variables are independent of each other. In this paper, we construct a decision tree from diabetes dataset in which it selects attributes at each other node of the tree like graph and model, each branch represents an outcome of the test, and each node hold a class attribute. This technique separates observation into branches to construct tree. In this technique tree is split in a recursive way called recursive partitioning. Decision tree is widely used in various areas because it is good enough for dataset distribution. For example, by using ID3 (Decision tree) algorithm we get a result like they are belong to diabetes or not. Method: We will use Naïve Bayes for probabilistic classification and ID3 for decision tree. Results: The dataset is related to Diabetes dataset. There are 18 columns like – Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. and 623 rows. Naive Bayes Classifier algorithm will be used for getting the probability of having diabetes or not. Here Diabetes is the class for Diabetes data set. There are two conditions “Yes” and “No” and have some personal information about the patient like - Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. We will see the probability that for “Yes” what unit of probability and for “No” what unit of probability which is given bellow. For Example: Gender – Female have 0.4964 for “No” and 0.5581 for “Yes” and for Male 0.5035 is for “No” and 0.4418 for “Yes”. Conclusions: In this paper two algorithms had been implemented Naive Bayes Classifier algorithm and ID3 algorithm. From Naive Bayes Classifier algorithm, the probability of having diabetes has been predicted and from ID3 algorithm a decision tree has been generated.

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