Abstract—The paper proposes a unique procedure to reduce risk factors in risk assessment. It offers a variant of decision tree called Identification tree for reducing number of risk factors used in assessment. The model, which uses auto insurance as a case study, employs historical evidences of different vehicles as risk factors. The work offers reduction of original risk factors from a set of twenty three to a reduced set of nine risk factors. The model was validated using real time and industry specific data.
Index Terms—Average disorder score, identification tree, risk assessment, risk factor.
Ankit Agarwal and Jyotsna Dongerdive are with the University Department of Computer Science, University of Mumbai, India (e-mail: ankit.g.agarwal@gmail.com, jyotss.d@gmail.com).
Siby Abraham is with the Department of Mathematics & Statistics, Guru Nanak Khalsa College, University of Mumbai, India (e-mail: sibyam@gmail.com).
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Cite:Ankit Agarwal, Jyotshna Dongardive, and Siby Abraham, "Reduction of Risk Factors in Risk Assessment: An Identification Tree Approach," International Journal of Modeling and Optimization vol. 3, no. 2, pp. 167-171, 2013.
