Benchmarking the Effectiveness of Classification Algorithms and SVM
  Kernels for Dry Beans
Anant Mehta Prajit Sengupta Divisha Garg Harpreet Singh Yosi Shacham Diamand
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.