A hybrid genetic algorithm for feature subset selection in rough set theory

Jing, Siyuan

doi:10.1007/s00500-013-1150-3

Cited by 53 publications

(23 citation statements)

References 33 publications

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“…Tenfold cross validation method applied to estimate the classification accuracy of the feature subset obtained. These results are compared with results of the existing [1] approach as shown in Table 3. The worst time complexity of the RST based local search operation used in the existing approach (HGARSTAR) is O(knm 3 ), where k is the number of iteration, n is number of instances and m is number of features.…”

Section: (3) Updating Velocity Of Swarm Takes O(mt)mentioning

confidence: 99%

“…Hence the computational cost of the proposed approach is lesser O(nm 2 ) as compared to the existing approach (O(knm 3 )). Also as given in [1] the RST based local search operation gives higher computational cost, but the PSO based local search operation as used in proposed approach gives less complexity with lower computational cost. By applying RGAP the classification accuracy of each dataset is also improved.…”

Section: (3) Updating Velocity Of Swarm Takes O(mt)mentioning

confidence: 99%

“…For an arbitrary set P ⊆ X, an indiscernibility relation is defined as given in equation (1). The lower approximation of a set X with respect to R is the set of all objects, which can be for certain classified as X with respect to R (are certainly X with respect to R).…”

Section: Rough Set Theorymentioning

confidence: 99%

“…The proposed approach provides efficient result but gives high complexity and computational cost. Si-Yuan Jing in his paper [1] has discussed a hybrid approach "HGARSTAR", by combining genetic algorithm and rough set theory for performing feature selection. Initially the core features are computed using RST based positive region method.…”

Section: Introductionmentioning

confidence: 99%

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RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

Gupta¹,

Purohit²

2017

IJCA

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Feature selection plays an important role in improving the classification accuracy by handling redundant or irrelevant features present in the dataset. Various soft computing based hybrid approaches like neuro-fuzzy, genetic-fuzzy, rough set-neuro etc. are proposed by researchers to perform feature selection. The existing approaches gives higher complexity and computational cost with low classification accuracy. Hence to improve the complexity and classification accuracy, a hybrid approach based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Rough Set Theory (RST) to perform feature selection is proposed. In the proposed approach, GA is used as a searching algorithm. To explore search space more efficiently, GA is combined with a PSO based local search operation. Rough Set Attribute Reduction (RSAR) method based on RST is used to compute core reducts. The proposed algorithm is tested on various benchmark datasets. Satisfactory improvements in terms of complexity and classification accuracy have been achieved.

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Section: (3) Updating Velocity Of Swarm Takes O(mt)mentioning

confidence: 99%

Section: (3) Updating Velocity Of Swarm Takes O(mt)mentioning

confidence: 99%

Section: Rough Set Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

Gupta¹,

Purohit²

2017

IJCA

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“…Rough set theory can effectively remove large amounts of redundant information existing in the training samples, and extract important feature so as to reduce the vector dimensions of sample data and the training time of neural network [12].The existing reduction algorithm, mainly from the nucleus of rough set, uses heuristic search method to construct a reduction which contains the minimum condition attributes, but the scope of application of this algorithm is limited and complex [13].…”

mentioning

confidence: 99%

A Study of Digit Recognition Algorithm for Meter based on Rough Set and Neural Network

Zhang¹,

Zhong²,

Shen³

et al. 2016

IJSH

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An exact feature selection algorithm based on rough set theory

2014

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Feature reduction based on rough set theory is an effective feature selection method in pattern recognition applications. Finding a minimal subset of the original features is inherent in rough set approach to feature selection. As feature reduction is a Nondeterministic Polynomial-time-hard problem, it is necessary to develop fast optimal or nearoptimal feature selection algorithms. This article aims to propose an exact feature selection algorithm in rough set that is efficient in terms of computation time. The proposed algorithm begins the examination of a solution tree by a breadth-first strategy. The pruned nodes are held in a version of the trie data structure. Based on the monotonic property of dependency degree, all subsets of the pruned nodes cannot be optimal solutions. Thus, by detecting these subsets in trie, it is not necessary to calculate their dependency degree. The search on the tree continues until the optimal solution is found. This algorithm is improved by selecting an initial search level determined by the hillclimbing method instead of searching the tree from the level below the root. The length of the minimal reduct and the size of data set can influence which starting search level is more efficient. The experimental results using some of the standard UCI data sets, demonstrate that the proposed algorithm is effective and efficient for data sets with more than 30 features. V C 2014 Wiley Periodicals, Inc. Complexity 20: 50-62, 2015

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A hybrid genetic algorithm for feature subset selection in rough set theory

Cited by 53 publications

References 33 publications

RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

A Study of Digit Recognition Algorithm for Meter based on Rough Set and Neural Network

An exact feature selection algorithm based on rough set theory

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