Memetic Evolution of Classification Ensembles

Creating classification ensembles may be perceived as a regularization technique which aims at improving the generalization capabilities of a classifier. In this paper, we introduce a multi-level memetic algorithm for evolving classification ensembles (they can be either homo- or heterogeneous). First, we evolve the content of such ensembles, and then we optimize the weights (both for the classifiers and for different classes) exploited while voting. The experimental study showed that our memetic algorithm retrieves high-quality heterogeneous ensembles, and can effectively deal with small training sets in multi-class classification.

[1]  George D. C. Cavalcanti,et al.  Online pruning of base classifiers for Dynamic Ensemble Selection , 2017, Pattern Recognit..

[2]  George D. C. Cavalcanti,et al.  Dynamic classifier selection: Recent advances and perspectives , 2018, Inf. Fusion.

[3]  Francisco Herrera,et al.  Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme , 2017, Knowl. Based Syst..

[4]  Bogdan Gabrys,et al.  Genetic algorithms in classifier fusion , 2006, Appl. Soft Comput..

[5]  Alan Wee-Chung Liew,et al.  Heterogeneous classifier ensemble with fuzzy rule-based meta learner , 2018, Inf. Sci..

[6]  Marek Kurzynski,et al.  A probabilistic model of classifier competence for dynamic ensemble selection , 2011, Pattern Recognit..

[7]  Xiuping Jia,et al.  Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning , 2018, Remote. Sens..

[8]  Ludmila I. Kuncheva,et al.  Clustering-and-selection model for classifier combination , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[9]  Johan A. K. Suykens,et al.  EnsembleSVM: a library for ensemble learning using support vector machines , 2014, J. Mach. Learn. Res..

[10]  Robert Sabourin,et al.  A dynamic overproduce-and-choose strategy for the selection of classifier ensembles , 2008, Pattern Recognit..

[11]  Jakub Nalepa,et al.  Segmentation of Hyperspectral Images Using Quantized Convolutional Neural Networks , 2018, 2018 21st Euromicro Conference on Digital System Design (DSD).

[12]  Emilio Corchado,et al.  A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.

[13]  Francisco Herrera,et al.  Dynamic ensemble selection for multi-class imbalanced datasets , 2018, Inf. Sci..

[14]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[15]  D C CavalcantiGeorge,et al.  Dynamic classifier selection , 2018 .

[16]  Luiz Eduardo Soares de Oliveira,et al.  Dynamic selection of classifiers - A comprehensive review , 2014, Pattern Recognit..

[17]  Aytug Onan,et al.  A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification , 2016, Expert Syst. Appl..

[18]  Stephen L. Smith,et al.  Evolving Ensembles: What Can We Learn from Biological Mutualisms? , 2015, IPCAT.