Entropy based C4.5-SHO algorithm with information gain optimization in data mining.

ABSTRACT: Information efficiency is gaining more importance in the development as well as application sectors of information technology. Data mining is a computer-assisted process of massive data investigation that extracts meaningful information from the datasets. The mined information is used in decision-making to understand the behavior of each attribute. Therefore, a new classification algorithm is introduced in this paper to improve information management. The classical C4.5 decision tree approach is combined with the Selfish Herd Optimization (SHO) algorithm to tune the gain of given datasets. The optimal weights for the information gain will be updated based on SHO. Further, the dataset is partitioned into two classes based on quadratic entropy calculation and information gain. Decision tree

SUBMITTER: Reddy GS 

PROVIDER: S-EPMC8049126 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature