Predicting sexual offenders using exhaustive CHAID techniques on victim's age. (19th February 2022)

• Record Type: Journal Article
• Authors: Kaur, Bhajneet; Ahuja, Laxmi; Kumar, Vinay
• Is Part Of: International journal of applied management science. Volume 14:Number 1(2022)
• Page Start: 71
• Page End: 89
• Publication Date: 2022-02-19
• Subjects: sexual offenders -- minor victim -- major victim -- decision tree -- index values -- gain chart -- response chart -- machine learning -- CHAID -- SPSS -- exhaustive CHAID; Management science -- Periodicals; 658
• Languages: English
• ISSNs: 1755-8913

Abstract

Sexual offences can spoil the whole culture of the society. This research paper proposes two decision models to classify and predict the sexual offenders of minor and major victims on the basis of their physical attributes namely age, race, weight and height using CHAID and Exhaustive CHAID techniques of decision tree. Overall dataset has been divided into 70:30 for building and testing the models. As resulted 79.8% rate of accuracy found by model using CHAID technique even model tested with 79.1% rate of accuracy. By using Exhaustive CHAID, 79.9% rate of accuracy depicts by the model developed through 70% of test data and model validated through 30% of test data with 78.8% rate of accuracy. The proposed models can help to take any kind of decision further by police departments, sexual harassment cells and law enforcement agencies for security purposes.