QUALITY PREDICTION OF WATERMELON USING RANKING FEATURE SELECTION METHODS AND MACHINE LEARNING ALGORITHMS

Abstract

This study was performed on the aim of detecting the quality of the watermelon with eight features; sound, color, root, belly button, texture, sugar rate, density, and touch which were obtained from the Kaggle website. Two ranking feature selection methods; ReliefF Ranking Filter and Information Gain Ranking Filter, and six machine learning algorithms; Decision Table (DT), J48 Tree (J48), Na¨ıve Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) accordingly have been employed for the Feature Selection and Classification Model (FS-CM) to predict the quality of this fruit. Evaluation process has been conducted with five features which were selected under Information Gain Ranking filter. The metric Accuracy and ROC area were used for the evaluation and hence, MLP with IG was selected as the best model with the highest accuracy of 87.0813 detect the quality of the watermelon.