Chaotic binary reptile search algorithm and its feature selection applications

被引：14

作者：

Abualigah L. ^{[1
,2
,5
,6
]}

Diabat A. ^{[3
,4
]}

机构：

[1] Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman

[2] Faculty of Information Technology, Middle East University, Amman

[3] Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi

[4] Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, 11201, NY

[5] Faculty of Computer Sciences and Informatics, Amman Arab University, Amman

[6] School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang

来源：

Journal of Ambient Intelligence and Humanized Computing | 2023年 / 14卷 / 10期

关键词：

Binary optimization problem; Chaotic maps; Feature selection (FS); Meta-heuristic algorithm; Reptile Search Algorithm (RSA);

D O I：

10.1007/s12652-022-04103-5

中图分类号：

学科分类号：

摘要：

Feature selection (FS) is known as the most challenging problem in the Machine Learning field. FS can be considered an optimization problem that requires an efficient method to prepare its optimal subset of relative features. This article introduces a new FS method-based wrapper scheme that mixes chaotic maps (CMs) and binary Reptile Search Algorithm (RSA) called CRSA, employed to address various FS problems. In this method, different chaotic maps are included with the main ideas of the RSA algorithm. The objective function is revealed to combine three objectives: maximizing the classification accuracy, the number of chosen features, and the complexity of produced wrapper models. To assess the achievement of the proposed methods, 20 UCI datasets are applied, and the results are compared with other well-known methods. The results showed the superiority of the introduced method in bettering other well-known techniques, particularly when applying binary RSA with Tent CM. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：13931 / 13947

页数：16

共 61 条

[1]

Abdel-Basset M., El-Shahat D., El-henawy I., de Albuquerque V.H.C., Mirjalili S., A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection, Expert Syst Appl, 139, (2020)

[2]

Abualigah L., Abd Elaziz M., Sumari P., Geem Z.W., Gandomi A.H., Reptile search algorithm (Rsa): A nature-inspired meta-heuristic optimizer, Expert Systems with Applications, (P, (2021)

[3]

Abualigah L., Diabat A., Chaotic binary group search optimizer for feature selection, Expert Syst Appl, 192, (2022)

[4]

Abualigah L., Diabat A., Elaziz M.A., Improved slime mould algorithm by opposition-based learning and levy flight distribution for global optimization and advances in real-world engineering problems, J Amb Intell Hum Comput, pp. 1-40, (2021)

[5]

Abualigah L., Diabat A., Mirjalili S., Abd Elaziz M., Gandomi A.H., The arithmetic optimization algorithm, Comput Methods Appl Mech Eng, 376, (2021)

[6]

Abualigah L., Yousri D., Abd Elaziz M., Ewees A.A., Al-qaness M.A., Gandomi A.H., Aquila optimizer: a novel meta-heuristic optimization algorithm, Comput Ind Eng, 157, (2021)

[7]

Abualigah L.M., Khader A.T., Al-Betar M.A., Alomari O.A., Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering, Expert Syst Appl, 84, pp. 24-36, (2017)

[8]

Abualigah L.M.Q., Feature selection and enhanced krill herd algorithm for text document clustering, (2019)

[9]

Agrawal R., Kaur B., Sharma S., Quantum based whale optimization algorithm for wrapper feature selection, Appl Soft Comput, (2020)

[10]

Aljarah I., Ala'M A.-Z., Faris H., Hassonah M.A., Mirjalili S., Saadeh H., Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm, Cogn Comput, 10, pp. 478-495, (2018)

← 1 2 3 4 5 6 7 →