Optimized Ensembled Machine Learning Model for IRIS Plant Classification

Abstract

Pattern recognition is one of the major concerns in the field of machine learning. Appropriate Recognition of patterns leads to addressing the performance concerns. Machine Learning [ML], a subset of Artificial Intelligence [AI] plays an important role in the process of classification, clustering and predictive modeling. This paper focuses on developing a novel classification technique to classify the iris of the plant in order to categorize the flower pattern. An optimized ensemble model is proposed to recognize and categorize the pattern. Initially, Decision Tree, OneR, Adaboost, Random Forest, and Bayesnet models are applied and to improve the performance an ensemble model is proposed.