A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data

Abstract

In today's world, agricultural production and operation activities generate a lot of data. As a result, computer-aided agriculture applications have become a hot topic in the study, with various machine learning (ML) algorithms being used to classify agricultural data. This paper presents a comparative study consisting of a combination of ML algorithms with meta-heuristic algorithms for feature selection to improve the classification capability of ML algorithms by finding the features that significantly impact accuracy. We have used six different meta-heuristic algorithms for feature selection. Experiments are conducted on four different agricultural datasets with five classification models. To understand the effect of proposed models, the selected features are fed into the ML algorithms. The results prove that combining ML and meta-heuristic algorithms achieves higher classification accuracy with fewer features on agricultural datasets.