Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans

TL;DR

This paper evaluates various SVM kernels and classification algorithms on the Dry Bean dataset, highlighting the RBF kernel's superior accuracy and providing guidance for agricultural data analysis.

Contribution

It systematically compares SVM kernels and other classifiers on agricultural data, emphasizing the importance of kernel choice for complex datasets.

Findings

RBF SVM achieved 93.34% accuracy

PCA improved classification performance

Different SVM kernels vary significantly in effectiveness

Abstract

Plant breeders and agricultural researchers can increase crop productivity by identifying desirable features, disease resistance, and nutritional content by analysing the Dry Bean dataset. This study analyses and compares different Support Vector Machine (SVM) classification algorithms, namely linear, polynomial, and radial basis function (RBF), along with other popular classification algorithms. The analysis is performed on the Dry Bean Dataset, with PCA (Principal Component Analysis) conducted as a preprocessing step for dimensionality reduction. The primary evaluation metric used is accuracy, and the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept visualization and empirical analysis, this study offers valuable guidance by emphasizing the importance of considering different SVM algorithms for complex and non-linear structured datasets.