A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

A Novel Memetic Feature Selection Algorithm

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods

Target detection with morphological shared-weight neural network : different update approaches

Neural networks are widely used for image processing. Of these, the convolutional neural network (CNN) is one of the most popular. However, the CNN needs a large amount of training data to improve its accuracy. If training data is limited, a morphological shared-weight neural network (MSNN) can be a better choice. In this thesis, two different update approaches based on an evolutionary algorithm are proposed and compared to each other for target detection based on the MSNN. Another network training, based on back propagation, is used for comparisons in this thesis, which was proposed by Yongwan Won and applied by my colleague and fellow graduate student, Shuxian Shen and Anes Ouadou. Single-layer and multiple-layer MSNNs are both presented with different approaches. For a dataset, the author created part of a dataset for this thesis and used another dataset created by Shen to make comparisons with her network. Results of the MSNN are compared with CNN results to show the performance. Experiments show that for a single-layer MSNN, the performance of an evolutionary algorithm with partial backpropagation is the best. For a multiple layer MSNN, backpropagation performs better, although the MSNN still has a better performance than the CNN.Includes bibliographical reference

GenoMeMeMusic: A Memetic-based Framework for Discovering the Musical Genome

(Abstract to follow