Evaluating Effectiveness of Classification Algorithms on Personality Prediction Dataset

Abstract

Classification in machine learning, refers to the process of categorizing given input data pieces into certain given groups. There are different types of classification algorithms that are widely used based on bayes, trees, functions or rules. The competence of these algorithmic methods has been a major issue since a long time and has caught the interests of a large researching community. In this paper we study the effectiveness of Rule-Based classifiers. There are several algorithms for rule classifier including Ridor, DTNB, JRip, OneR, NNge, ZeroR and many more. This paper presents a comparative analysis of Decision Table and Conjunctive Rule, two different classification algorithm, to classify and predict personality based on the Big Five Model dataset and supports the same with implementation results on WEKA.