Academic literature on the topic 'Cost-sensitive classification'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Cost-sensitive classification.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Cost-sensitive classification"
Wang, Jialei, Peilin Zhao, and Steven C. H. Hoi. "Cost-Sensitive Online Classification." IEEE Transactions on Knowledge and Data Engineering 26, no. 10 (October 2014): 2425–38. http://dx.doi.org/10.1109/tkde.2013.157.
Full textZhang, Shichao. "Cost-sensitive KNN classification." Neurocomputing 391 (May 2020): 234–42. http://dx.doi.org/10.1016/j.neucom.2018.11.101.
Full textZhao, Peilin, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, and Junzhou Huang. "Adaptive Cost-Sensitive Online Classification." IEEE Transactions on Knowledge and Data Engineering 31, no. 2 (February 1, 2019): 214–28. http://dx.doi.org/10.1109/tkde.2018.2826011.
Full textCebe, Mumin, and Cigdem Gunduz-Demir. "Qualitative test-cost sensitive classification." Pattern Recognition Letters 31, no. 13 (October 2010): 2043–51. http://dx.doi.org/10.1016/j.patrec.2010.05.028.
Full textZhang, Shichao. "Cost-sensitive classification with respect to waiting cost." Knowledge-Based Systems 23, no. 5 (July 2010): 369–78. http://dx.doi.org/10.1016/j.knosys.2010.01.008.
Full textPendharkar, Parag C. "Linear models for cost-sensitive classification." Expert Systems 32, no. 5 (June 5, 2015): 622–36. http://dx.doi.org/10.1111/exsy.12114.
Full textJi, Shihao, and Lawrence Carin. "Cost-sensitive feature acquisition and classification." Pattern Recognition 40, no. 5 (May 2007): 1474–85. http://dx.doi.org/10.1016/j.patcog.2006.11.008.
Full textYang, Yi, Yuxuan Guo, and Xiangyu Chang. "Angle-based cost-sensitive multicategory classification." Computational Statistics & Data Analysis 156 (April 2021): 107107. http://dx.doi.org/10.1016/j.csda.2020.107107.
Full textTapkan, Pınar, Lale Özbakır, Sinem Kulluk, and Adil Baykasoğlu. "A cost-sensitive classification algorithm: BEE-Miner." Knowledge-Based Systems 95 (March 2016): 99–113. http://dx.doi.org/10.1016/j.knosys.2015.12.010.
Full textWang, Tao, Zhenxing Qin, Shichao Zhang, and Chengqi Zhang. "Cost-sensitive classification with inadequate labeled data." Information Systems 37, no. 5 (July 2012): 508–16. http://dx.doi.org/10.1016/j.is.2011.10.009.
Full textDissertations / Theses on the topic "Cost-sensitive classification"
Dachraoui, Asma. "Cost-Sensitive Early classification of Time Series." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLA002/document.
Full textEarly classification of time series is becoming increasingly a valuable task for assisting in decision making process in many application domains. In this setting, information can be gained by waiting for more evidences to arrive, thus helping to make better decisions that incur lower misclassification costs, but, meanwhile, the cost associated with delaying the decision generally increases, rendering the decision less attractive. Making early predictions provided that are accurate requires then to solve an optimization problem combining two types of competing costs. This thesis introduces a new general framework for time series early classification problem. Unlike classical approaches that implicitly assume that misclassification errors are cost equally and the cost of delaying the decision is constant over time, we cast the the problem as a costsensitive online decision making problem when delaying the decision is costly. We then propose a new formal criterion, along with two approaches that estimate the optimal decision time for a new incoming yet incomplete time series. In particular, they capture the evolutions of typical complete time series in the training set thanks to a segmentation technique that forms meaningful groups, and leverage these complete information to estimate the costs for all future time steps where data points still missing. These approaches are interesting in two ways: (i) they estimate, online, the earliest time in the future where a minimization of the criterion can be expected. They thus go beyond the classical approaches that myopically decide at each time step whether to make a decision or to postpone the call one more time step, and (ii) they are adaptive, in that the properties of the incoming time series are taken into account to decide when is the optimal time to output a prediction. Results of extensive experiments on synthetic and real data sets show that both approaches successfully meet the behaviors expected from early classification systems
MARQUES, DANIEL DOS SANTOS. "A DECISION TREE LEARNER FOR COST-SENSITIVE BINARY CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28239@1.
Full textCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Problemas de classificação foram amplamente estudados na literatura de aprendizado de máquina, gerando aplicações em diversas áreas. No entanto, em diversos cenários, custos por erro de classificação podem variar bastante, o que motiva o estudo de técnicas de classificação sensível ao custo. Nesse trabalho, discutimos o uso de árvores de decisão para o problema mais geral de Aprendizado Sensível ao Custo do Exemplo (ASCE), onde os custos dos erros de classificação variam com o exemplo. Uma das grandes vantagens das árvores de decisão é que são fáceis de interpretar, o que é uma propriedade altamente desejável em diversas aplicações. Propomos um novo método de seleção de atributos para construir árvores de decisão para o problema ASCE e discutimos como este pode ser implementado de forma eficiente. Por fim, comparamos o nosso método com dois outros algoritmos de árvore de decisão propostos recentemente na literatura, em 3 bases de dados públicas.
Classification problems have been widely studied in the machine learning literature, generating applications in several areas. However, in a number of scenarios, misclassification costs can vary substantially, which motivates the study of Cost-Sensitive Learning techniques. In the present work, we discuss the use of decision trees on the more general Example-Dependent Cost-Sensitive Problem (EDCSP), where misclassification costs vary with each example. One of the main advantages of decision trees is that they are easy to interpret, which is a highly desirable property in a number of applications. We propose a new attribute selection method for constructing decision trees for the EDCSP and discuss how it can be efficiently implemented. Finally, we compare our new method with two other decision tree algorithms recently proposed in the literature, in 3 publicly available datasets.
Bakshi, Arjun. "Methodology For Generating High-Confidence Cost-Sensitive Rules For Classification." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868085.
Full textKamath, Vidya P. "Enhancing Gene Expression Signatures in Cancer Prediction Models: Understanding and Managing Classification Complexity." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3653.
Full textJulock, Gregory Alan. "The Effectiveness of a Random Forests Model in Detecting Network-Based Buffer Overflow Attacks." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/190.
Full textMakki, Sara. "An Efficient Classification Model for Analyzing Skewed Data to Detect Frauds in the Financial Sector." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1339/document.
Full textThere are different types of risks in financial domain such as, terrorist financing, money laundering, credit card fraudulence and insurance fraudulence that may result in catastrophic consequences for entities such as banks or insurance companies. These financial risks are usually detected using classification algorithms. In classification problems, the skewed distribution of classes also known as class imbalance, is a very common challenge in financial fraud detection, where special data mining approaches are used along with the traditional classification algorithms to tackle this issue. Imbalance class problem occurs when one of the classes have more instances than another class. This problem is more vulnerable when we consider big data context. The datasets that are used to build and train the models contain an extremely small portion of minority group also known as positives in comparison to the majority class known as negatives. In most of the cases, it’s more delicate and crucial to correctly classify the minority group rather than the other group, like fraud detection, disease diagnosis, etc. In these examples, the fraud and the disease are the minority groups and it’s more delicate to detect a fraud record because of its dangerous consequences, than a normal one. These class data proportions make it very difficult to the machine learning classifier to learn the characteristics and patterns of the minority group. These classifiers will be biased towards the majority group because of their many examples in the dataset and will learn to classify them much faster than the other group. After conducting a thorough study to investigate the challenges faced in the class imbalance cases, we found that we still can’t reach an acceptable sensitivity (i.e. good classification of minority group) without a significant decrease of accuracy. This leads to another challenge which is the choice of performance measures used to evaluate models. In these cases, this choice is not straightforward, the accuracy or sensitivity alone are misleading. We use other measures like precision-recall curve or F1 - score to evaluate this trade-off between accuracy and sensitivity. Our objective is to build an imbalanced classification model that considers the extreme class imbalance and the false alarms, in a big data framework. We developed two approaches: A Cost-Sensitive Cosine Similarity K-Nearest Neighbor (CoSKNN) as a single classifier, and a K-modes Imbalance Classification Hybrid Approach (K-MICHA) as an ensemble learning methodology. In CoSKNN, our aim was to tackle the imbalance problem by using cosine similarity as a distance metric and by introducing a cost sensitive score for the classification using the KNN algorithm. We conducted a comparative validation experiment where we prove the effectiveness of CoSKNN in terms of accuracy and fraud detection. On the other hand, the aim of K-MICHA is to cluster similar data points in terms of the classifiers outputs. Then, calculating the fraud probabilities in the obtained clusters in order to use them for detecting frauds of new transactions. This approach can be used to the detection of any type of financial fraud, where labelled data are available. At the end, we applied K-MICHA to a credit card, mobile payment and auto insurance fraud data sets. In all three case studies, we compare K-MICHA with stacking using voting, weighted voting, logistic regression and CART. We also compared with Adaboost and random forest. We prove the efficiency of K-MICHA based on these experiments
Charnay, Clément. "Enhancing supervised learning with complex aggregate features and context sensitivity." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD025/document.
Full textIn this thesis, we study model adaptation in supervised learning. Firstly, we adapt existing learning algorithms to the relational representation of data. Secondly, we adapt learned prediction models to context change.In the relational setting, data is modeled by multiples entities linked with relationships. We handle these relationships using complex aggregate features. We propose stochastic optimization heuristics to include complex aggregates in relational decision trees and Random Forests, and assess their predictive performance on real-world datasets.We adapt prediction models to two kinds of context change. Firstly, we propose an algorithm to tune thresholds on pairwise scoring models to adapt to a change of misclassification costs. Secondly, we reframe numerical attributes with affine transformations to adapt to a change of attribute distribution between a learning and a deployment context. Finally, we extend these transformations to complex aggregates
Lo, Hung-Yi, and 駱宏毅. "Cost-Sensitive Multi-Label Classification with Applications." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/61015886145358618517.
Full text國立臺灣大學
資訊工程學研究所
101
We study a generalization of the traditional multi-label classification, which we refer to as cost-sensitive multi-label classification (CSML). In this problem, the misclassification cost can be different for each instance-label pair. For solving the problem, we propose two novel and general strategies based on the problem transformation technique. The proposed strategies transform the CSML problem to several cost-sensitive single-label classification problems. In addition, we propose a basis expansion model for CSML, which we call the Generalized k-Labelsets Ensemble (GLE). In the basis expansion model, a basis function is a label powerset classifier trained on a random k-labelset. The expansion coefficients are learned by minimizing the cost-weighted global error between the prediction and the ground truth. GLE can also be used for traditional multi-label classification. Experimental results on both multi-label classification and cost-sensitive multi-label classification demonstrate that our method has better performance than other methods. Cost-sensitive classification is based on the assumption that the cost is given according to the application. “Where does cost come from?” is an important practical issue. We study two real-world prediction tasks and link their data distribution to the cost information. The two tasks are medical image classification and social tag prediction. In medical image classification, we observe a patient-imbalanced phenomenon that has seriously hurt the generalization ability of the image classifier. We design several patient-balanced learning algorithms based on cost-sensitive binary classification. The success of our patient-balanced learning methods has been proved by winning KDD Cup 2008. For social tag prediction, we propose to treat the tag counts as the mis-classification costs and model the social tagging problem as a cost-sensitive multi-label classification problem. The experimental results in audio tag annotation and retrieval demonstrate that the CSML approaches outperform our winning method in Music Information Retrieval Evaluation eXchange (MIREX) 2009 in terms of both cost-sensitive and cost-less evaluation metrics. The results on social bookmark prediction also demonstrate that our proposed method has better performance than other methods.
Sun, Yanmin. "Cost-Sensitive Boosting for Classification of Imbalanced Data." Thesis, 2007. http://hdl.handle.net/10012/3000.
Full textTu, Han-Hsing, and 涂漢興. "Regression approaches for multi-class cost-sensitive classification." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/79841686006299558588.
Full text國立臺灣大學
資訊工程學研究所
97
Cost-sensitive classification is an important research problem in recent years. It allows machine learning algorithms to use the additional cost information to make more strategic decisions. Studies on binary cost-sensitive classification have led to promising results in theories, algorithms, and applications. The multi-class counterpart is also needed in many real-world applications, but is more difficult to analyze. This thesis focuses on multi-class cost-sensitive classification. Existing methods for multi-class cost-sensitive classification usually transform the cost information into example importance (weight). This thesis offers a different viewpoint of the problem, and proposes a novel method. We directly estimate the cost value corresponding to each prediction using regression, and outputs the label that comes with the smallest estimated cost. We improve the method by analyzing the errors made during the decision. Then, we propose a different regression loss function that tightly connects with the errors. The new loss function leads to a solid theoretical guarantee of error transformation. We design a concrete algorithm for the loss function with the support vector machines. The algorithm can be viewed as a theoretically justified extension the popular one-versus-all support vector machine. Experiments using real-world data sets with arbitrary cost values demonstrate the usefulness of our proposed methods, and validate that the cost information should be appropriately used instead of dropped.
Book chapters on the topic "Cost-sensitive classification"
Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Cost-Sensitive Classification." In Encyclopedia of Machine Learning, 231. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_180.
Full textRoychoudhury, Shoumik, Mohamed Ghalwash, and Zoran Obradovic. "Cost Sensitive Time-Series Classification." In Machine Learning and Knowledge Discovery in Databases, 495–511. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_30.
Full textQin, Zhenxing, Chengqi Zhang, Tao Wang, and Shichao Zhang. "Cost Sensitive Classification in Data Mining." In Advanced Data Mining and Applications, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_1.
Full textMitrokotsa*, Aikaterini, Christos Dimitrakakis, and Christos Douligeris. "Intrusion Detection Using Cost-Sensitive Classification." In Proceedings of the 3rd European Conference on Computer Network Defense, 35–47. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-85555-4_3.
Full textQin, Zhenxing, Alan Tao Wang, Chengqi Zhang, and Shichao Zhang. "Cost-Sensitive Classification with k-Nearest Neighbors." In Knowledge Science, Engineering and Management, 112–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39787-5_10.
Full textIša, Jiří, Zuzana Reitermanová, and Ondřej Sýkora. "Cost-Sensitive Classification with Unconstrained Influence Diagrams." In SOFSEM 2012: Theory and Practice of Computer Science, 625–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27660-6_51.
Full textWang, Yu, and Nan Wang. "Study on an Extreme Classification of Cost - Sensitive Classification Algorithm." In Advances in Intelligent Systems and Computing, 1772–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2568-1_250.
Full textDavis, Jason V., Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, and Emmett Witchel. "Cost-Sensitive Decision Tree Learning for Forensic Classification." In Lecture Notes in Computer Science, 622–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11871842_60.
Full textKotsiantis, Sotiris B., and Panagiotis E. Pintelas. "A Cost Sensitive Technique for Ordinal Classification Problems." In Methods and Applications of Artificial Intelligence, 220–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24674-9_24.
Full textMargineantu, Dragos D. "Class Probability Estimation and Cost-Sensitive Classification Decisions." In Lecture Notes in Computer Science, 270–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_23.
Full textConference papers on the topic "Cost-sensitive classification"
Wang, Jialei, Peilin Zhao, and Steven C. H. Hoi. "Cost-Sensitive Online Classification." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.116.
Full textSchaefer, Gerald, Bartosz Krawczyk, Niraj P. Doshi, and Tomoharu Nakashima. "Cost-sensitive texture classification." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900500.
Full textTur, Gokhan. "Cost-sensitive call classification." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-41.
Full textLiu, Zhenbing, Chunyang Gao, Huihua Yang, and Qijia He. "Cost-sensitive sparse representation based classification." In 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2016. http://dx.doi.org/10.1109/ccis.2016.7790248.
Full textAli, Alnur, and Kevyn Collins-Thompson. "Robust Cost-Sensitive Confidence-Weighted Classification." In 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. http://dx.doi.org/10.1109/icdmw.2013.108.
Full textNan, Feng, Joseph Wang, Kirill Trapeznikov, and Venkatesh Saligrama. "Fast margin-based cost-sensitive classification." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854141.
Full textSchaefer, G., T. Nakashima, Y. Yokota, and H. Ishibuchi. "Cost-Sensitive Fuzzy Classification for Medical Diagnosis." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221238.
Full textAzab, Ahmad, Robert Layton, Mamoun Alazab, and Paul Watters. "Skype Traffic Classification Using Cost Sensitive Algorithms." In 2013 Fourth Cybercrime and Trustworthy Computing Workshop (CTC). IEEE, 2013. http://dx.doi.org/10.1109/ctc.2013.11.
Full textO'Brien, Deirdre B., Maya R. Gupta, and Robert M. Gray. "Cost-sensitive multi-class classification from probability estimates." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390246.
Full textBakshi, Arjun, and Raj Bhatnagar. "Learning Cost-Sensitive Rules for Non-forced Classification." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.62.
Full textReports on the topic "Cost-sensitive classification"
Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.
Full text