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NEATER: filtering of over-sampled data using non-cooperative game theory

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Buy a game neater

Postby Mezik В» 24.03.2019

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Specifically, the problem is formulated as a non-cooperative game where all the data are players and the goal is to uniformly and consistently label all of the synthetic data created by any over-sampling technique. The proposed algorithm does not require any prior assumptions and selects representative synthetic instances while generating a very small number of noisy data.

We present extensive experimental results over a large collection of datasets using three different classifiers to demonstrate the advantages of our method.

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Comput Stat Data Anal 51 12 — In: Proceedings of the international conference of pattern recognition, Stockholm, Sweden in press. Proc Natl Acad Sci 96 12 — Pattern Recogn 36 3 — BMC Bioinform 14 1 Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem.

In: Springer ed Advances in knowledge discovery and data mining. Springer, New York, pp — J Artif Intell Res — In: Proceedings of the 2nd international conference on computer automation engineering, Singapore, pp — PLOS Genet 5 8 :e Artif Intell Med 37 1 :7— Cressman R The stability concept of evolutionary game theory: a dynamic approach.

Springer-Verlag, New York. J Mach Learn Res — Swarm Evol Comput 1 1 :3— Erdem A, Pelillo M Graph transduction as a noncooperative game. Neural Comput 24 3 — Pattern Recogn Lett 27 8 — Evol Comput 17 3 — Inf Sci 10 — Knowl Based Syst 25 1 — Cancer Res 62 17 — NIPS workshop on feature extraction and feature selection. Springer, New York.

Adv Intell Comput Springer — Hart PE The condensed nearest neighbour rule. He H, Garcia E Learning from imbalanced data. Hofbauer J, Sigmund K Evolutionary game dynamics. Bull Am Math Soc 40 4 In: Proceedings of the 11th international joint conference on artificial intelligence, vol 1, Detroit.

Hu B, Dong W A study on cost behaviors of binary classification measures in class-imbalanced problems. Howson TJ Equilibria of polymatrix games. Manag Sci — Kreps DM Game theory and economic modelling. Clarendon, Oxford. Kubat M, Matwin S Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th international conference on machine learning, pp — Laurikkala J Improving identification of difficult small classes by balancing class distribution.

Artif Intell Med 63— J Soc Ind Appl Math 12 2 — Lemnaru C, Rodica P Imbalanced classification problems: systematic study, issues and best practices. In: Springer ed Enterprise information systems. Springer, New York, pp 35— Lusa L, Blagus R Class prediction for high-dimensional class-imbalanced data.

BMC Bioinform 11 1 Inf Sci — Nash J Non-cooperative games. Ann Math 54 2 — Cambridge University Press, Cambridge. Oh S Error back-propagation algorithm for classification of imbalanced data. Neurocomputing 74 6 — Ordeshook PC Game theory and political theory: an introduction. Games Econ Behav 63 2 — Knowl Inf Syst 33 2 — Games Econ Behav 71 1 — Smith J Evolution and the theory of games.

Sokolova M, Lapalme G A systematic analysis of performance measures for classification tasks. Inf Process Manag 45 4 — Proc Natl Acad Sci — Weibull JW Evolutionary game theory. MIT Press, London. BMC Bioinform 7 1 Yoon K, Kwek S An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics.

In: Proceedings of the hybrid intelligent systems, Rio de Janeiro, p 6. J Comput Inf Syst 7 6 — Download references. Correspondence to I. This appendix provides seven tables with the detailed results for the experimental analysis carried out in the present work.

Table 12 contains the AUC values for all the databases and algorithms achieved when using the C4. Tables 15 , 16 and 17 contain the AUC values for all high-dimensional datasets for the three classifiers. The best results are highlighted in bold face.

Table 18 contains the full description of the datasets used for the experimental analysis. Reprints and Permissions. Almogahed, B. Soft Comput 19, — Download citation. Published : 19 October Issue Date : November Search SpringerLink Search. Immediate online access to all issues from Subscription will auto renew annually. Taxes to be calculated in checkout.

In: Proceedings of the 11th international joint conference on artificial intelligence, vol 1, Detroit Hu B, Dong W A study on cost behaviors of binary classification measures in class-imbalanced problems.

In: Proceedings of the 14th international conference on machine learning, pp — Laurikkala J Improving identification of difficult small classes by balancing class distribution.

Cambridge University Press, Cambridge Oh S Error back-propagation algorithm for classification of imbalanced data. Kakadiaris Authors B. Almogahed View author publications. You can also search for this author in PubMed Google Scholar.

View author publications. Additional information Communicated by V. Appendix Appendix This appendix provides seven tables with the detailed results for the experimental analysis carried out in the present work.

Table 12 AUC results for C4. Table 13 AUC results for random forest classifier Full size table.

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Re: buy a game neater

Postby Vusar В» 24.03.2019

Table 13 AUC results for random forest classifier Full size table. Made in China. Kubat M, Matwin S Addressing the curse of imbalanced training z one-sided selection. Card Game.

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Re: buy a game neater

Postby Mausar В» 24.03.2019

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Re: buy a game neater

Postby Gorg В» 24.03.2019

The best results are highlighted in bold face. Comput Stat Data Anal 51 12 — NIPS workshop on feature extraction and feature selection.

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Re: buy a game neater

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Proc Natl Acad Sci 96 12 — Lemnaru C, Rodica P Imbalanced http://castdraw.site/download-games/download-games-baggage-size.php problems: systematic study, issues and best practices. Download references. Table 16 AUC results on high-dimensional data for random forest classifier Full size table.

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