Open AccessA Novel Categorical Data Attribute Split Technique in Decision Tree Learning
Author(s) -
D. Mabuni*
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2568.059120
Subject(s) - categorical variable , decision tree , similarity (geometry) , data mining , tree (set theory) , class (philosophy) , computer science , aggregate (composite) , artificial intelligence , mathematics , pattern recognition (psychology) , metric (unit) , machine learning , mathematical analysis , operations management , materials science , composite material , economics , image (mathematics)
A new technique is proposed for splitting categorical data during the process of decision tree learning. This technique is based on the class probability representations and manipulations of the class labels corresponding to the distinct values of categorical attributes. For each categorical attribute aggregate similarity in terms of class probabilities is computed and then based on the highest aggregated similarity measure the best attribute is selected and then the data in the current node of the decision tree is divided into the number of sub sets equal to the number of distinct values of the best categorical split attribute. Many experiments are conducted using this proposed method and the results have shown that the proposed technique is better than many other competitive methods in terms of efficiency, ease of use, understanding, and output results and it will be useful in many modern applications.