Determining the classification of dry beans using WEKA
Paper Details
Determining the classification of dry beans using WEKA
Abstract
The significant variety in bean shape and size makes dry bean classification a difficult task. Traditional categorization procedures, such as hand sorting, are time-consuming and labor-intensive. Machine learning approaches appear to be a potential option for dry bean classification. A machine learning classification approach for dry beans using WEKA was offered in this research. The approach employs Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Decision Tree (DT) machine learning algorithms. The method was evaluated on a dataset of 13,611 dry beans from seven different varieties shared by Koklu, M. and Ozkan, I.A., (2020) extracted from the UCI machine learning repository. The approaches attained an accuracy of 90.30%, 90.83%, 92.23% and 92.49% for k-nearest neighbor, decision tree, support vector machine and multilayer perceptron respectively. The multilayer perceptron has the highest accuracy while the k-nearest neighbor has the lowest accuracy performance. The suggested method offers various advantages over conventional dry bean classification methods. It is more accurate, faster, and requires less labor. The method can also be used to classify dry beans that are difficult to identify visually. According to the findings of this study, machine learning approaches provide a potential strategy for dry bean classification. The proposed method can be utilized to automate the dry bean classification process, which could result in considerable efficiency and accuracy gains and the algorithm that performs the best accuracy may be used for classifications of Dry Beans in the Philippines.
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