Feature Selection Using K-Means Genetic Algorithm for Multi-objective Optimization

Anusha, M.; Sathiaseelan, J. G. R.

doi:10.1016/j.procs.2015.07.387

Cited by 39 publications

(19 citation statements)

References 7 publications

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“…It is noted that the algorithm could produce minimum index value for the maximum datasets. However, there is a need for proper feature selection for better, more optimal solution [14,15]. Ruby et al [16] suggested two methods for ranking of MOPs.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

Anusha¹,

Sathiaseelan²

2016

ijacsa

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Abstract-There is huge amount of data available in health industry which is found difficult in handing, hence mining of data is necessary to innovate the hidden patterns and their relevant features. Recently, many researchers have devoted to the study of using data mining on disease diagnosis. Mining biomedical data is one of the predominant research area where evolutionary algorithms and clustering techniques are emphasized in diabetes disease diagnosis. Therefore, this research focuses on application of evolution clustering multiobjective optimization algorithm (ECMO) to analyze the data of patients suffering from diabetes disease. The main objective of this work is to maximize the prediction accuracy of cluster and computation efficiency along with minimum cost for data clustering. The experimental results prove that this application has attained maximum accuracy for dataset of Pima Indians Diabetes from UCI repository. In this way, by analyzing the three objectives, ECMO could achieve best Pareto fronts.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

Anusha¹,

Sathiaseelan²

2016

ijacsa

Self Cite

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“…Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. Different genetic algorithm (GA) methods have been proposed to tackle the feature selection [5,6]. A hybrid algorithm was proposed using GA with the artificial neural networks (ANN) where GA was used as a pre-step to reduce the feature size [7].…”

Section: Introductionmentioning

confidence: 99%

Firefly Optimization Using Artificial Immune System for Feature Subset Selection

Kihel¹,

Chouraqui²

2019

IJIES

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At first glance, the feature selection is a crucial step in a pattern recognition system. The main objective of this selection is to reduce the features number, by eliminating irrelevant and redundant attributes. In addition, we try to maintain or improve the classifier performance using neural network algorithm. Nevertheless, a new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for feature subset selection. We have used the firefly and clonal selection algorithms to select the most relevant features in a dataset. In our proposed strategy, feature selection algorithm is formulated as an optimization problem that searches an optimum with less number of features in a feature space and a good accuracy. The goal of our study is to achieve a balance between the classification accuracy and the size of the feature subsets selected using two new hybrid algorithms based on Immune Firefly Algorithm (IFA). Our proposed approach has been evaluated on 10 standard datasets taken from UCI repository. The experimental outcomes have been compared to some popular feature selection methods. The comparison of results shows that our methods significantly outperform most of the used feature selection algorithms.

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“…A disadvantage of K-Means is that it is easy to fall into local optima. As a remedy, a popular trend is to integrate the genetic algorithm [7,8] with K-means to obtain genetic K-means algorithms [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. K-Means is also combined with fuzzy mechanism to obtain fuzzy C-Means [24,25].…”

Section: Introductionmentioning

confidence: 99%

A Genetic XK-Means Algorithm with Empty Cluster Reassignment

Hua

Zhang

et al. 2019

Symmetry

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K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means).

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Feature Selection Using K-Means Genetic Algorithm for Multi-objective Optimization

Cited by 39 publications

References 7 publications

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

Firefly Optimization Using Artificial Immune System for Feature Subset Selection

A Genetic XK-Means Algorithm with Empty Cluster Reassignment

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