Voting Based Neural Network Extreme Learning Machine Essay

Extreme learning Machine (ELM) [1] is a single hidden layer feed forward network (SLFN) introduced by G. B. Huang in 2006. In ELM, the weights between input and hidden neurons and the bias for each hidden neuron are assigned randomly. The weight between output neurons and hidden neurons are generated using the Moore Penrose Generalized Inverse [18]. This makes ELM a fast learning classifier. It surmounts various traditional gradient based learning algorithms [1] such as Back Propagation (BP) and well known classifier Support Vector Machine (SVM) . In order to improve the performance various variants of the ELM came over time such as Enhanced Incremental ELM (EI-ELM)[2], Optimal Pruned ELM (OP-ELM) [3], Convex Incremental ELM (CI-ELM)[4],†¦show more content†¦Mainly ensemble pruning [12] approaches are categorized into three types. a). Ordering Based Pruning: In this pruning approach the classifiers are arranged using some criteria and some of the top classifiers are selected as a Pruned Ensemble (PE). Some of the Ordering Based Pruning approaches are as follows: Kappa Pruning [12], Reduce Error Pruning [12], Minimum Distance Minimization Pruning(MDP) [12], Pruning via Individual Contribution Ordering [13], Ensemble Pruning Using Spectral Coefficient [14]. b) Optimization based pruning is a pruning approach which uses evolutionary techniques for pruning such as Genetic Algorithm (GA). A fitness function is genetically optimized to get a subset of classifiers which minimizes the error. Various variants of genetic based ensemble pruning have been proposed such as Genetic Algorithm based Selective Neural Network Ensemble (GASEN) [15], GAB: EPA [16]. Objective of GASEN is to select the best PE and maximize the accuracy of the PE by assigning the best weight to the classifiers of the PE. It uses fitness function, which is function of the generalization error minimized by genetic algorithm. GAB:EPA [16] was proposed for handling multiclass imbalanced data sets, diversity factor was also incorporated in fitness function to improve the performance. c) Cluster Based Pruning Technique: In such type of pruning technique many clusters of the component classifiers are made and fromShow MoreRelatedDecision Tree Induction Clustering Techniques in Sas Enterprise Miner, Spss Clementine, and Ibm Intelligent Miner – a Comparative Analysis6636 Words   |  27 Pagesto choose the most appropriate software for a particular organization. This paper aims to provide a comparative analysis for three popular data mining software tools, which are SAS ® Enterprise Miner, SPSS Clementine, and IBM DB2 ® Intelligent Miner based on four main criteria, which are performance, functionality, usability, and auxiliary Task Support. 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