Feature selection using multi-objective CHC genetic algorithm

Rathee, Seema; Ratnoo, Saroj

doi:10.1016/j.procs.2020.03.376

Cited by 13 publications

(2 citation statements)

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“…Then a second layer is added to the proposed technique to eliminate any remaining redundant/irrelevant predictors to improve the prediction accuracy. Rathee and Ratnoo [70] proposed a genetic algorithm-based multi-objective method for feature selection. This method combines the idea of non-dominated sorting with a genetic algorithm to arrive at a set of non-dominated solutions.…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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“…In this technique, searching subsets are used around the space of search or by itself; they generate solutions. Heuristic Search techniques include evolutionary algorithms, like the Genetic Algorithm (GA) [6] and CHC Genetic Algorithm (CHCGA) [10,11]. Another method that is also used in the wrapper technique is the embedded technique.…”

Section: 2mentioning

confidence: 99%

A Survey on Feature Selection Techniques using Evolutionary Algorithms

Ansari¹

2021

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Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the preferred feature matters in complex and massive data. Here, furthermore, I discuss algorithms like the genetic algorithm (GA), the Non-Dominated Sorting Genetic Algorithm (NSGA-II), Particle Swarm Optimization (PSO), and some other meta-heuristic strategies for considering the provisional separation of issues. A comparison of these algorithms has been performed; the results show that the feature selection technique benefits machine learning algorithms by improving the performance of the algorithm. This paper also presents various real-world applications of using feature selection.

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