Authors: A. Suruliandi; H. Benjamin Fredrick David; S.P. Raja
Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India ' Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India
Abstract: Decision tree is a renowned classifier reformed by innumerable tree generation techniques to construct efficient trees. In this paper, a novel framework for constructing decision tree classifiers using ranked attributes and weights is presented. The tree is fabricated using proposed framework hierarchically from root to leaf using ranks and weights of attributes assigned to branches based on their contribution towards classification accuracy. The weights are perpetually updated until the tree furnishes maximum classification accuracy. This framework is experimented with cuckoo search, firefly search, wolf search and proposed limited lazy wolf search for ranking attributes and the experimental results illustrates the framework achieving excellent performance than the antecedent. For demonstrating the stability of proposed framework in all problems, numerous experiments are carried out using prominent benchmark datasets and a proposed crime propensity prediction dataset. The crime propensity prediction accuracy accomplished by fine tuning the proposed algorithm was 99.3617% directing crime free society.
Keywords: data mining; classification; decision trees; DTs; crime prediction; crime propensity.
International Journal of Applied Decision Sciences, 2020 Vol.13 No.1, pp.46 - 73
Received: 12 Feb 2019
Accepted: 01 Mar 2019
Published online: 26 Dec 2019 *