Optimal feature selection for classification using rough set-based CGA-NN classifier

Nancy, S. Gilbert; Appavu, S.

doi:10.1504/ijbidm.2016.082212

Cited by 4 publications

(2 citation statements)

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“…[51] introduced An efficient FS approach using Modified Social Spider Optimization (MSSO) algorithm and [52] investigated the unsupervised feature selection for enhancing the performance of classification models. Further, the authors of [53] applied FS combined with CGA-NN classifier for the optimal solution. Also, more various studies employed the FS methods in the literature such as [54]- [58].…”

Section: Review Of Related Workmentioning

confidence: 99%

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

et al. 2021

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Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.

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Section: Review Of Related Workmentioning

confidence: 99%

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

et al. 2021

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“…Menghour and Souici-Meslati (2014) proposed swarm based approach for selecting best features for spam filtering. Gilbert Nancy and Appavu (2016) proposed multiple kernel fuzzy C-mean clustering algorithm for feature selection to improve SVM classifier. Gunavathi and Premalatha (2015) used cuckoo search optimisation techniques for finding best features from the original dataset.…”

Section: Introductionmentioning

confidence: 99%

Feature selection and instance selection using cuttlefish optimisation algorithm through tabu search

Karunakaran¹,

Suganthi²,

Rajasekar³

2020

IJENM

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Over the recent decades, the amount of data generated has been growing exponentially, the existing machine learning algorithms are not feasible for processing of such huge amount of data. To solve such kind of issues, we have two commonly adopted schemes, one is scaling up the data mining algorithms and other one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this paper, cuttlefish optimisation algorithm along with tabu search approach is used for data reduction. Dataset can be reduced mainly in two ways, one is the selecting optimal subset of features from the original dataset, in other words eliminating those features which are contributing lesser information another method is selecting optimal subset of instances from the original data set, in other words eliminating those instances which are contributing lesser information. Cuttlefish optimisation algorithm with tabu search finds both optimal subset of features and instances. Optimal subset of feature and instance obtained from the cuttlefish algorithm with tabu search provides a similar detection rate, accuracy rate, lesser false positive rate and the lesser computational time for training the classifier that we obtained from the original data set.

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An Efficient Feature Selection and Classification Using Optimal Radial Basis Function Neural Network

Balamurugan¹,

Nancy²

2018

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Feature selection is the process of identifying and removing many irrelevant and redundant features. Irrelevant features, along with redundant features, severely affect the accuracy of the learning machines. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise. And also, time complexity is the major issues in existing approach. In order to rectify these issues our proposed method made use of efficient feature subset selection in high dimensional data. Here we are considering the input dataset is the high dimensional micro array dataset. Initially, we have to select the optimal features so that our proposed technique employed Modified Social Spider Optimization (MSSO) algorithm. Here the traditional Social Spider Optimization is modified with the help of fruit fly optimization algorithm. Next the selected features are the input for the classifier. Here the classification is performed using Optimized Radial basis Function based neural network (ORBFNN) technique to classify the micro array data as normal or abnormal data. The effectiveness of RBFNN is optimized by means of artificial bee colony algorithm (ABC). Experimental results indicate that the proposed classification framework have outperformed by having better accuracy for five benchmark dataset 93.66%, 97.09%, 98.66%, 98.28% and 98.93% which is minimum value when compared to the existing technique. The proposed method is executed in MATLAB platform.

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Optimal feature selection for classification using rough set-based CGA-NN classifier

Cited by 4 publications

References 0 publications

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

Feature selection and instance selection using cuttlefish optimisation algorithm through tabu search

An Efficient Feature Selection and Classification Using Optimal Radial Basis Function Neural Network

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