|
Aggarwal C. C. 2013. An introduction to outlier analysis. In Outlier Analysis., Springer, 1–40.
Google Scholar
|
|
Angiulli F. & Fassetti F. 2014. Exploiting domain knowledge to detect outliers. Data Mining and Knowledge Discovery 28(2), 519–568.
Google Scholar
|
|
Angiulli F. & Pizzuti C. 2005. Outlier mining in large high-dimensional data sets. IEEE Transactions on Knowledge and Data Engineering 17(2), 203–215.
Google Scholar
|
|
Aridas C. K., Kotsiantis S. B. & Vrahatis M. N. 2016. Combining prototype selection with local boosting. In Artificial Intelligence Applications and Innovations (AIAI) 2016. IFIP Advances in Information and Communication Technology, Iliadis, L. & Maglogiannis, I. (eds). Springer, 475.
Google Scholar
|
|
Aridas C. K., Kotsiantis S. B. & Vrahatis M. N. 2017. Hybrid local boosting utilizing unlabeled data in classification tasks. Evolving Systems 1–11.
Google Scholar
|
|
Arnaiz-González Á., Dez-Pastor J.-F., RodríGuez J. J. & Garca-Osorio C. 2016. Instance selection of linear complexity for big data. Knowledge-Based Systems 107, 83–95.
Google Scholar
|
|
Augasta M. G. & Kathirvalavakumar T. 2012. A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier. Applied Soft Computing 12(2), 619–625.
Google Scholar
|
|
Batista G. E., Prati R. C. & Monard M. C. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6(1), 20–29.
Google Scholar
|
|
Bezdek J. C. & Kuncheva L. I. 2001. Nearest prototype classifier designs: an experimental study. International Journal of Intelligent Systems 16(12), 1445–1473.
Google Scholar
|
|
Boulle M. 2004. KHIOPS: a statistical discretization method of continuous attributes. Machine Learning 55(1), 53–69.
Google Scholar
|
|
Brodley C. E. & Friedl M. A. 1999. Identifying mislabeled training data. Journal of Artificial Intelligence Research 11, 131–167.
Google Scholar
|
|
Buzzi-Ferraris G. & Manenti F. 2011. Outlier detection in large data sets. Computers & Chemical Engineering 35(2), 388–390.
Google Scholar
|
|
Caises Y., González A., Leyva E. & Pérez R. 2011. Combining instance selection methods based on data characterization: an approach to increase their effectiveness. Information Sciences 181(20), 4780–4798.
Google Scholar
|
|
Cano A., Nguyen D. T., Ventura S. & Cios K. J. 2016. ur-CAIM: improved CAIM discretization for unbalanced and balanced data. Soft Computing 20(1), 173–188.
Google Scholar
|
|
Cano J.-R., Garca S. & Herrera F. 2008. Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes. Pattern Recognition Letters 29(16), 2156–2164.
Google Scholar
|
|
Cano J. R., Herrera F. & Lozano M. 2005. Strategies for scaling up evolutionary instance reduction algorithms for data mining. In Evolutionary Computation in Data Mining, Springer, 21–39.
Google Scholar
|
|
Caruana R. & de Sa V. R. 2003. Benefitting from the variables that variable selection discards. Journal of Machine Learning Research 3(3), 1245–1264.
Google Scholar
|
|
Chawla N. V., Bowyer K. W., Hall L. O. & Kegelmeyer W. P. 2002. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357.
Google Scholar
|
|
Chen S., Wang W. & van Zuylen H. 2010a. A comparison of outlier detection algorithms for ITS data. Expert Systems with Applications 37(2), 1169–1178.
Google Scholar
|
|
Chen Y., Miao D. & Zhang H. 2010b. Neighborhood outlier detection. Expert Systems with Applications 37(12), 8745–8749.
Google Scholar
|
|
Chow T. W. & Huang D. 2005. Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information. IEEE Transactions on Neural Networks 16(1), 213–224.
Google Scholar
|
|
Cismondi F., Fialho A. S., Vieira S. M., Reti S. R., Sousa J. M. & Finkelstein S. N. 2013. Missing data in medical databases: impute, delete or classify? Artificial Intelligence in Medicine 58(1), 63–72.
Google Scholar
|
|
Crone S. F., Lessmann S. & Stahlbock R. 2006. The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research 173(3), 781–800.
Google Scholar
|
|
Czarnowski I. 2010. Prototype selection algorithms for distributed learning. Pattern Recognition 43(6), 2292–2300.
Google Scholar
|
|
Czarnowski I. 2012. Cluster-based instance selection for machine classification. Knowledge and Information Systems 30(1), 113–133.
Google Scholar
|
|
de Haro-García A. & García-Pedrajas N. 2009. A divide-and-conquer recursive approach for scaling up instance selection algorithms. Data Mining and Knowledge Discovery 18(3), 392–418.
Google Scholar
|
|
de Sá C. R., Soares C. & Knobbe A. 2016. Entropy-based discretization methods for ranking data. Information Sciences 329, 921–936.
Google Scholar
|
|
Delany S. J., Segata N. & Mac Namee B. 2012. Profiling instances in noise reduction. Knowledge-Based Systems 31, 28–40.
Google Scholar
|
|
Derrac J., Garca S. & Herrera F. 2010a. IFS-CoCo: instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recognition 43(6), 2082–2105.
Google Scholar
|
|
Derrac J., Garca S. & Herrera F. 2010b. A survey on evolutionary instance selection and generation. International Journal of Applied Metaheuristic Computing 1, 60–92.
Google Scholar
|
|
Dougherty J., Kohavi R. & Sahami M. 1995. Supervised and unsupervised discretization of continuous features. In Machine Learning Proceedings 1995, 194–202. Elsevier.
Google Scholar
|
|
Ekambaram R., Fefilatyev S., Shreve M., Kramer K., Hall L. O., Goldgof D. B. & Kasturi R. 2016. Active cleaning of label noise. Pattern Recognition 51, 463–480.
Google Scholar
|
|
Elomaa T. & Rousu J. 2004. Efficient multisplitting revisited: optima-preserving elimination of partition candidates. Data Mining and Knowledge Discovery 8(2), 97–126.
Google Scholar
|
|
Escalante H. J. 2005. A comparison of outlier detection algorithms for machine learning. In Proceedings of the International Conference on Communications in Computing, 228–237.
Google Scholar
|
|
Estabrooks A., Jo T. & Japkowicz N. 2004. A multiple resampling method for learning from imbalanced data sets. Computational Intelligence 20(1), 18–36.
Google Scholar
|
|
Farhangfar A., Kurgan L. & Dy J. 2008. Impact of imputation of missing values on classification error for discrete data. Pattern Recognition 41(12), 3692–3705.
Google Scholar
|
|
Farhangfar A., Kurgan L. & Pedrycz W. 2007. A novel framework for imputation of missing values in databases. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 37(5), 692–709.
Google Scholar
|
|
Farquad M. & Bose I. 2012. Preprocessing unbalanced data using support vector machine. Decision Support Systems 53(1), 226–233.
Google Scholar
|
|
Fernández A., Carmona C. J., del Jesus M. & Herrera F. 2017. A pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets. International Journal of Neural Systems 27, 1–17.
Google Scholar
|
|
Filzmoser P., Maronna R. & Werner M. 2008. Outlier identification in high dimensions. Computational Statistics & Data Analysis 52(3), 1694–1711.
Google Scholar
|
|
Flores M. J., Gámez J. A., Martnez A. M. & Puerta J. M. 2011. Handling numeric attributes when comparing bayesian network classifiers: does the discretization method matter? Applied Intelligence 34(3), 372–385.
Google Scholar
|
|
Galar M., Fernández A., Barrenechea E., Bustince H. & Herrera F. 2011. An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition 44(8), 1761–1776.
Google Scholar
|
|
Garcia L. P., de Carvalho A. C. & Lorena A. C. 2016a. Noise detection in the meta-learning level. Neurocomputing 176, 14–25.
Google Scholar
|
|
Garcia L. P., Lorena A. C., Matwin S. & de Carvalho A. C. 2016b. Ensembles of label noise filters: a ranking approach. Data Mining and Knowledge Discovery 30(5), 1192–1216.
Google Scholar
|
|
García S., Cano J. R. & Herrera F. 2008. A memetic algorithm for evolutionary prototype selection: a scaling up approach. Pattern Recognition 41(8), 2693–2709.
Google Scholar
|
|
García S., Derrac J., Cano J. & Herrera F. 2012a. Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435.
Google Scholar
|
|
García S., Luengo J. & Herrera F. 2015. Data Preprocessing in Data Mining. Springer.
Google Scholar
|
|
García S., Luengo J., Sáez J. A., Lopez V. & Herrera F. 2013. A survey of discretization techniques: taxonomy and empirical analysis in supervised learning. IEEE Transactions on Knowledge and Data Engineering 25(4), 734–750.
Google Scholar
|
|
García V., Sánchez J. S. & Mollineda R. A. 2012b. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowledge-Based Systems 25(1), 13–21.
Google Scholar
|
|
García-Pedrajas N., Del Castillo J. A. R. & Ortiz-Boyer D. 2010. A cooperative coevolutionary algorithm for instance selection for instance-based learning. Machine Learning 78(3), 381–420.
Google Scholar
|
|
GarcíA-Pedrajas N. & PéRez-RodríGuez J. 2012. Multi-selection of instances: a straightforward way to improve evolutionary instance selection. Applied Soft Computing 12(11), 3590–3602.
Google Scholar
|
|
Ghoting A., Parthasarathy S. & Otey M. E. 2008. Fast mining of distance-based outliers in high-dimensional datasets. Data Mining and Knowledge Discovery 16(3), 349–364.
Google Scholar
|
|
Gonzalez-Abril L., Cuberos F. J., Velasco F. & Ortega J. A. 2009. AMEVA: an autonomous discretization algorithm. Expert Systems with Applications 36(3), 5327–5332.
Google Scholar
|
|
Gupta A., Mehrotra K. G. & Mohan C. 2010. A clustering-based discretization for supervised learning. Statistics & Probability Letters 80(9), 816–824.
Google Scholar
|
|
Guyon I. & Elisseeff A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3(3), 1157–1182.
Google Scholar
|
|
He H. & Garcia E. A. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284.
Google Scholar
|
|
Hernandez-Leal P., Carrasco-Ochoa J. A., Martnez-Trinidad J. F. & Olvera-Lopez J. A. 2013. Instancerank based on borders for instance selection. Pattern Recognition 46(1), 365–375.
Google Scholar
|
|
Hinton G. E. & Salakhutdinov R. R. 2006. Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507.
Google Scholar
|
|
Hoffmann H. 2007. Kernel PCA for novelty detection. Pattern Recognition 40(3), 863–874.
Google Scholar
|
|
Honghai F., Guoshun C., Cheng Y., Bingru Y. & Yumei C. 2005. A SVM regression based approach to filling in missing values. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 581–587. Springer.
Google Scholar
|
|
Hu Q., Che X., Zhang L. & Yu D. 2010. Feature evaluation and selection based on neighborhood soft margin. Neurocomputing 73(10), 2114–2124.
Google Scholar
|
|
Hua J., Tembe W. D. & Dougherty E. R. 2009. Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognition 42(3), 409–424.
Google Scholar
|
|
Hua J., Xiong Z., Lowey J., Suh E. & Dougherty E. R. 2005. Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21(8), 1509–1515.
Google Scholar
|
|
Huang H., Lin J., Chen C. & Fan M. 2006. Review of outlier detection. Application Research of Computers 8, 2006–2008.
Google Scholar
|
|
Janssens D., Brijs T., Vanhoof K. & Wets G. 2006. Evaluating the performance of cost-based discretization versus entropy-and error-based discretization. Computers & Operations Research 33(11), 3107–3123.
Google Scholar
|
|
Jerez J. M., Molina I., Garca-Laencina P. J., Alba E., Ribelles N., Martn M. & Franco L. 2010. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine 50(2), 105–115.
Google Scholar
|
|
Jin R., Breitbart Y. & Muoh C. 2009. Data discretization unification. Knowledge and Information Systems 19(1), 1–29.
Google Scholar
|
|
Kennedy J. & Eberhart R. C. 1995. Particle swarm optimization. In IEEE International Conference on Neural Networks Proceedings 1995, 4, 1942–1948. IEEE.
Google Scholar
|
|
Kim S., Cho N. W., Kang B. & Kang S.-H. 2011. Fast outlier detection for very large log data. Expert Systems with Applications 38(8), 9587–9596.
Google Scholar
|
|
Kim S.-W. & Oommen B. J. 2003. A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Analysis & Applications 6(3), 232–244.
Google Scholar
|
|
Klinkenberg R. 2004. Learning drifting concepts: example selection vs. example weighting. Intelligent Data Analysis 8(3), 281–300.
Google Scholar
|
|
Kumar A. & Zhang D. 2007. Hand-geometry recognition using entropy-based discretization. IEEE Transactions on Information Forensics and Security 2(2), 181–187.
Google Scholar
|
|
Kurgan L. A. & Cios K. J. 2004. CAIM discretization algorithm. IEEE Transactions on Knowledge and Data Engineering 16(2), 145–153.
Google Scholar
|
|
Lemaître G., Nogueira F. & Aridas C. K. 2017. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research 18(17), 1–5.
Google Scholar
|
|
Li M., Deng S., Feng S. & Fan J. 2011. An effective discretization based on class-attribute coherence maximization. Pattern Recognition Letters 32(15), 1962–1973.
Google Scholar
|
|
Lin W.-C., Tsai C.-F., Ke S.-W., Hung C.-W. & Eberle W. 2015. Learning to detect representative data for large scale instance selection. Journal of Systems and Software 106, 1–8.
Google Scholar
|
|
Liu C., Wang W., Wang M., Lv F. & Konan M. 2017. An efficient instance selection algorithm to reconstruct training set for support vector machine. Knowledge-Based Systems 116, 58–73.
Google Scholar
|
|
Liu F. T., Ting K. M. & Zhou Z.-H. 2012. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data 6(1), 1–39.
Google Scholar
|
|
Liu H., Hussain F., Tan C. L. & Dash M. 2002. Discretization: an enabling technique. Data Mining and Knowledge Discovery 6(4), 393–423.
Google Scholar
|
|
Liu H. & Motoda H. 2002. On issues of instance selection. Data Mining and Knowledge Discovery 6(2), 115–130.
Google Scholar
|
|
Liu H. & Motoda H. 2007. Computational Methods of Feature Selection. CRC Press.
Google Scholar
|
|
Liu X. & Wang H. 2005. A discretization algorithm based on a heterogeneity criterion. IEEE Transactions on Knowledge and Data Engineering 17(9), 1166–1173.
Google Scholar
|
|
Liu Z.-G., Pan Q., Dezert J. & Martin A. 2016. Adaptive imputation of missing values for incomplete pattern classification. Pattern Recognition 52, 85–95.
Google Scholar
|
|
Lobato F., Sales C., Araujo I., Tadaiesky V., Dias L., Ramos L. & Santana A. 2015. Multi-objective genetic algorithm for missing data imputation. Pattern Recognition Letters 68, 126–131.
Google Scholar
|
|
López V., Fernández A., Moreno-Torres J. G. & Herrera F. 2012. Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics. Expert Systems with Applications 39(7), 6585–6608.
Google Scholar
|
|
Losing V., Hammer B. & Wersing H. 2018. Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 275, 1261–1274.
Google Scholar
|
|
Luengo J., Garca S. & Herrera F. 2012. On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowledge and Information Systems 32(1), 77–108.
Google Scholar
|
|
Mahanipour A., Nezamabadi-pour H. & Nikpour B. 2018. Using fuzzy-rough set feature selection for feature construction based on genetic programming. In 2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 1–6. IEEE.
Google Scholar
|
|
Maldonado S. & Weber R. 2009. A wrapper method for feature selection using support vector machines. Information Sciences 179(13), 2208–2217.
Google Scholar
|
|
Mao K. 2004. Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 629–634.
Google Scholar
|
|
Marchiori E. 2008. Hit miss networks with applications to instance selection. Journal of Machine Learning Research 9(6), 997–1017.
Google Scholar
|
|
Mateos-García D., García-Gutiérrez J. & Riquelme-Santos J. C. 2012. On the evolutionary optimization of k-NN by label-dependent feature weighting. Pattern Recognition Letters 33(16), 2232–2238.
Google Scholar
|
|
Mizianty M. J., Kurgan L. A. & Ogiela M. R. 2010. Discretization as the enabling technique for the naive Bayes and semi-naive Bayes-based classification. The Knowledge Engineering Review 25(04), 421–449.
Google Scholar
|
|
Moreno-Torres J. G., Raeder T., Alaiz-RodríGuez R., Chawla N. V. & Herrera F. 2012. A unifying view on dataset shift in classification. Pattern Recognition 45(1), 521–530.
Google Scholar
|
|
Nanni L. & Lumini A. 2011. Prototype reduction techniques: a comparison among different approaches. Expert Systems with Applications 38(9), 11820–11828.
Google Scholar
|
|
Nikolaidis K., Goulermas J. Y. & Wu Q. 2011. A class boundary preserving algorithm for data condensation. Pattern Recognition 44(3), 704–715.
Google Scholar
|
|
Olvera-López J. A., Carrasco-Ochoa J. A., Martnez-Trinidad J. F. & Kittler J. 2010. A review of instance selection methods. Artificial Intelligence Review 34(2), 133–143.
Google Scholar
|
|
Panday D., de Amorim R. C. & Lane P. 2018. Feature weighting as a tool for unsupervised feature selection. Information Processing Letters 129, 44–52.
Google Scholar
|
|
Park D.-C. 2009. Centroid neural network with weighted features. Journal of Circuits, Systems, and Computers 18(08), 1353–1367.
Google Scholar
|
|
Parsopoulos K. E. & Vrahatis M. N. 2010. Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Publishing (IGI Global).
Google Scholar
|
|
Pearson R. K. 2005. Mining Imperfect Data: Dealing with Contamination and Incomplete Records. SIAM.
Google Scholar
|
|
Piramuthu S. 2004. Evaluating feature selection methods for learning in data mining applications. European Journal of Operational Research 156(2), 483–494.
Google Scholar
|
|
Piramuthu S. & Sikora R. T. 2009. Iterative feature construction for improving inductive learning algorithms. Expert Systems with Applications 36(2), 3401–3406.
Google Scholar
|
|
Pkekalska E., Duin R. P. & Paclk P. 2006. Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189–208.
Google Scholar
|
|
Pyle D. 1999. Data Preparation for Data Mining, 1. Morgan Kaufmann.
Google Scholar
|
|
Qin Y., Zhang S., Zhu X., Zhang J. & Zhang C. 2009. POP algorithm: kernel-based imputation to treat missing values in knowledge discovery from databases. Expert Systems with Applications 36(2), 2794–2804.
Google Scholar
|
|
Quionero-Candela J., Sugiyama M., Schwaighofer A. & Lawrence N. D. 2009. Dataset Shift in Machine Learning. MIT Press.
Google Scholar
|
|
Ramírez-Gallego S., Garca S., Mouriño-Taln H., Martnez-Rego D., Bolón-Canedo V., Alonso-Betanzos A., Bentez J. M. & Herrera F. 2016. Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6(1), 5–21.
Google Scholar
|
|
Ramírez-Gallego S., Krawczyk B., Garca S., Woźniak M. & Herrera F. 2017. A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239, 39–56.
Google Scholar
|
|
Reinartz T. 2002. A unifying view on instance selection. Data Mining and Knowledge Discovery 6(2), 191–210.
Google Scholar
|
|
Sáez J. A., Galar M., Luengo J. & Herrera F. 2016. INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Information Fusion 27, 19–32.
Google Scholar
|
|
Sáez J. A., Luengo J. & Herrera F. 2013. Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognition 46(1), 355–364.
Google Scholar
|
|
Segata N., Blanzieri E., Delany S. J. & Cunningham P. 2010. Noise reduction for instance-based learning with a local maximal margin approach. Journal of Intelligent Information Systems 35(2), 301–331.
Google Scholar
|
|
Shen C., Wang X. & Yu D. 2012. Feature weighting of support vector machines based on derivative saliency analysis and its application to financial data mining. International Journal of Advancements in Computing Technology 4(1), 199–206.
Google Scholar
|
|
Shu W. & Shen H. 2016. Multi-criteria feature selection on cost-sensitive data with missing values. Pattern Recognition 51, 268–280.
Google Scholar
|
|
Silva-Ramírez E.-L., Pino-Mejas R., López-Coello M. & Cubiles-de-la Vega M.-D. 2011. Missing value imputation on missing completely at random data using multilayer perceptrons. Neural Networks 24(1), 121–129.
Google Scholar
|
|
Sim J., Kwon O. & Lee K. C. 2016. Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets. Expert Systems with Applications 46, 485–493.
Google Scholar
|
|
Skillicorn D. B. & McConnell S. M. 2008. Distributed prediction from vertically partitioned data. Journal of Parallel and Distributed Computing 68(1), 16–36.
Google Scholar
|
|
Smith M. G. & Bull L. 2005. Genetic programming with a genetic algorithm for feature construction and selection. Genetic Programming and Evolvable Machines 6(3), 265–281.
Google Scholar
|
|
Somol P. & Pudil P. 2002. Feature selection toolbox. Pattern Recognition 35(12), 2749–2759.
Google Scholar
|
|
Sun Y., Wong A. K. & Kamel M. S. 2009. Classification of imbalanced data: a review. International Journal of Pattern Recognition and Artificial Intelligence 23(04), 687–719.
Google Scholar
|
|
Triguero I., Derrac J., Garcia S. & Herrera F. 2012a. Integrating a differential evolution feature weighting scheme into prototype generation. Neurocomputing 97, 332–343.
Google Scholar
|
|
Triguero I., Derrac J., Garcia S. & Herrera F. 2012b. A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(1), 86–100.
Google Scholar
|
|
Tsai C.-F. & Chang F.-Y. 2016. Combining instance selection for better missing value imputation. Journal of Systems and Software 122, 63–71.
Google Scholar
|
|
Tsai C.-J., Lee C.-I. & Yang W.-P. 2008. A discretization algorithm based on class-attribute contingency coefficient. Information Sciences 178(3), 714–731.
Google Scholar
|
|
Unler A., Murat A. & Chinnam R. B. 2011. mr 2 PSO: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Information Sciences 181(20), 4625–4641.
Google Scholar
|
|
van Hulse J. & Khoshgoftaar T. 2009. Knowledge discovery from imbalanced and noisy data. Data & Knowledge Engineering 68(12), 1513–1542.
Google Scholar
|
|
van Hulse J. & Khoshgoftaar T. M. 2006. Class noise detection using frequent itemsets. Intelligent Data Analysis 10(6), 487–507.
Google Scholar
|
|
van Hulse J. D., Khoshgoftaar T. M. & Huang H. 2007. The pairwise attribute noise detection algorithm. Knowledge and Information Systems 11(2), 171–190.
Google Scholar
|
|
Virgolin M., Alderliesten T., Bel A., Witteveen C. & Bosman P. A. 2018. Symbolic regression and feature construction with gp-gomea applied to radiotherapy dose reconstruction of childhood cancer survivors. In Proceedings of the Genetic and Evolutionary Computation Conference, 1395–1402. ACM.
Google Scholar
|
|
Wang B. & Japkowicz N. 2004. Imbalanced data set learning with synthetic samples. In Proc. IRIS Machine Learning Workshop, 19.
Google Scholar
|
|
Wettschereck D., Aha D. W. & Mohri T. 1997. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review 11(1–5), 273–314.
Google Scholar
|
|
Wilson D. R. & Martinez T. R. 2000. Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286.
Google Scholar
|
|
Witten I. H., Frank E., Hall M. A. & Pal C. J. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
Google Scholar
|
|
Wong T.-T. 2012. A hybrid discretization method for nave bayesian classifiers. Pattern Recognition 45(6), 2321–2325.
Google Scholar
|
|
Wu X. & Zhu X. 2008. Mining with noise knowledge: error-aware data mining. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 38(4), 917–932.
Google Scholar
|
|
Yang Y., Webb G. I. & Wu X. 2009. Discretization methods. In Data Mining and Knowledge Discovery Handbook. Springer, 101–116.
Google Scholar
|
|
Zhang S. 2011. Shell-neighbor method and its application in missing data imputation. Applied Intelligence 35(1), 123–133.
Google Scholar
|
|
Zhang S., Jin Z. & Zhu X. 2011. Missing data imputation by utilizing information within incomplete instances. Journal of Systems and Software 84(3), 452–459.
Google Scholar
|
|
Zhang S., Zhang J., Zhu X., Qin Y. & Zhang C. 2008. Missing value imputation based on data clustering. In Transactions on Computational Science I, Springer, 128–138.
Google Scholar
|
|
Zhou M.-J. & Chen X.-J. 2012. An outlier mining algorithm based on dissimilarity. Procedia Environmental Sciences 12, 810–814.
Google Scholar
|
|
Zhou Y., Chen Y., Feng L., Zhang X., Shen Z. & Zhou X. 2018. Supervised and adaptive feature weighting for object-based classification on satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(9), 3224–3234.
Google Scholar
|
|
Zhu X. & Wu X. 2004. Class noise vs. attribute noise: a quantitative study. Artificial Intelligence Review 22(3), 177–210.
Google Scholar
|
