Research Area: Metaheuristic Computing
The recent advancements in science, engineering, and technology have facilitated huge generation of datasets. These huge datasets contain noisy, redundant, and irrelevant features which negatively affects the performance of classification techniques in machine learning and data mining process. Feature selection is a pre-processing stage for reducing the dimensionality of datasets by selecting the most important attributes while increasing the accuracy of classification at the same time. In this paper, we present a novel hybrid binary version of enhanced chaotic crow search and particle swarm optimization algorithm (ECCSPSOA) to solve feature selection problems. In the proposed ECCSPSOA, in order to navigate the feature space, we hybridized the enhanced version of the CSA algorithm which has a better search strategy and particle swarm optimization (PSO) which is capable of converging into the best global solution in the search field. We further embed opposition-based learning technique in the local search of the hybrid algorithm. The ECCSPSOA was compared using 15 datasets from the UCI repository with four well-known optimization algorithms, such as particle swarm optimization (PSO), binary particle swarm optimization (BPSO), crow search algorithm (CSA), and chaotic crow search algorithm (CCSA). In the experiments with k-Nearest Neighbour (KNN) as a classifier, six different performance metrics were used. To tackle the over-fitting problem, each dataset is divided into training and testing data using K-fold cross-validation. The computational findings demonstrate that the proposed algorithm obtains an average accuracy rate of 89.67 % over 15 datasets, indicating that our technique exceeds state-of-the-art findings in 12 of the 15 datasets studied. Furthermore, the suggested approach outperforms state-of-the-art methods in terms of fitness value and standard deviation, obtaining the lowest value in 13 and 8 of the datasets studied respectively.
Keywords:
science
engineering
technology
datasets
noisy
redundant
irrelevant features
dimensionality
particle swarm optimization
k-Nearest Neighbour
Author(s) Name: Abdulhameed Adamu, Mohammed Abdullahi, Sahalu Balarabe Junaidu, Ibrahim Hayatu Hassan
Journal name: Machine Learning with Applications
Conferrence name:
Publisher name: Elsevier
DOI: https://doi.org/10.1016/j.mlwa.2021.100108
Volume Information: Volume 6
