Fuzzy Classification to Classify the Income Category Based On Entropy

• Main Authors: Vaiyapuri Srinivasan, Rajenderan Govind, Vandar Kuzhali Jagannathan, Aruna Murugesan
• Format: Article
• Language: English
• Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2011-10-01
• Series: Journal of Computer Science and Technology
• Subjects: classification; entropy; information gain; id3; decision tree; fuzzy
• Online Access: https://journal.info.unlp.edu.ar/JCST/article/view/672

The classification problem is one of the main issues in data mining because it aims to extract a classifier which can be used to predict the classes of objects whose class table are unknown. This paper deals with classifying the income database with the entropy based method for analyzing the income is high or low. This method incorporates two mathematical techniques Entropy and Information Gain (IG) with Interactive Dichotomize 3 Algorithm (ID3). Subsets are calculated through Entropy. We fix the threshold point based on the fuzzy approach and the factors are identified using IG. The ID3 algorithm is used to derive a decision tree which classifies the income. This method also helps to extract logical rules that could be used in classifying high or low based on income with various attributed.