International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 6, December 2023, pp. 7037~7047 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp7037-7047  7037 Journal homepage: http://ijece.iaescore.com Feature selection for sky image classification based on self adaptive ant colony system algorithm Montha Petwan 1 , Ku Ruhana Ku-Mahamud 2,3 1 Faculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, Thailand 2 School of Computing, Universiti Utara Malaysia, Kedah, Malaysia 3 Department of Information and Communications Technology, College of Engineering, Shibaura Institute of Technology, Tokyo, Japan Article Info ABSTRACT Article history: Received Apr 26, 2023 Revised Jul 16, 2023 Accepted Jul 17, 2023 Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest, k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney U test are employed to statistically evaluate the efficiency of the proposed algorithms. Keywords: Ant colony system Bio-inspired algorithm Feature extraction Feature selection Sky image classification This is an open access article under the CC BY-SA license. Corresponding Author: Montha Petwan Faculty of Science and Technology, Suratthani Rajabhat University Khun Taleay, Muang, Surat Thani, 84100, Thailand Email: monta.pet@sru.ac.th 1. INTRODUCTION The classifying of the cloud type from ground-based sky images is continually receiving attention. The different forms of cloud have an impact on both weather prediction and the exchange of energy between the atmosphere and the Earth’s surface [1], [2]. The variations of cloud images which depend on various atmospheric circumstances are the primary distinction between cloud images and other images. A cloud does not always have a definite spatial distribution. Even clouds of the same genus can vary in size and shape. Additionally, sophisticated examples of curving shapes, crossing borders, and angles can be seen in the structure information and cloud distribution [3]–[5]. The various identification technology equipment to collect sky photographs include meteorological balloons, satellites-based, and ground-based [6], [7]. The meteorological balloon and satellite-based approach’s cloud-system enable the direct observation on how clouds affect the earth’s radiation at the top of the atmosphere. The purpose of a ground-based approach is to use the local area and observe cloud bottoms in order to get whole data of the