A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset

The paper presents a new hybrid ensemble approach consisting of a combination of machine learning algorithms, a feature ranking method and a supervised instance filter. Its aim is to improve the performance results of machine learning algorithms for multiclass classification problems. The performance of new hybrid ensemble approach is tested for its effectiveness over four standard agriculture multiclass datasets. It performs better on all these datasets. It is applied on multiclass oilseed disease dataset. It is observed that ensemble-Vote performs better than Logistic Regression and Naïve Bayes algorithms. The performance results of hybrid ensemble are compared with ensemble-Vote. The performance results prove that the new hybrid ensemble approach outperforms ensemble-Vote with improved oilseed disease classification accuracy up to 94.73%.