Particle Swarm Optimization based incremental classifier design for rice disease prediction

Abstract

Increase of huge amount of data in every application demands an incremental learning technique for data analysis. One of such data analysis task in dynamic environment is to design an incremental classifier for decision making and consequently updating the knowledge base of the overall system. Classifier construction depicts extraction of interesting patterns from the large repository of data and predicts the future trends based on the existing patterns. The time complexity of the classification system increases gradually and the system becomes inefficient while it is learned repeatedly for adding new group of data with the existing one in a certain interval of time. Without learning the same classifier for the whole data, if the knowledge of old data extracted by the classifier is used together with the new group of data to design the updated classifier, called incremental classifier, then time complexity reduces drastically. In the paper, the concepts of Particle Swarm Optimization technique and Association Rule Mining are used to design an incremental rule based classification system. The incremental classifier is suitable to apply on rice disease dataset for disease prediction as the characteristics of rice diseases change in time due to change of climate, biological, and geographical factors. The proposed method has been applied on both simulated rice disease dataset and benchmark datasets and the classification accuracy is measured and compared with various state of the art classification algorithms. The method is also evaluated based on some statistical measures and statistical test is done to establish its significance and effectiveness.