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The profit and loss of the insured person have been mainly associated with the risk of that person. The presence of the uneven distribution of this risk defines different characteristic and lifestyle of that person. Today because of lots of information gathering from technology and ease of availability of data, underwriting and policy-making in life insurance are possible by understanding risk factors from this uneven distribution. The presence of non-risk factors in this uneven distribution only contributes to adding the noise. This noise always increases execution time though not necessarily decrease the accuracy of machine-learning models. Time can be very crucial in real time system and its importance increases in higher dimensional feature space. This paper attempts to deal with such problem in life insurance industry without compromising the accuracy significantly in any machine-learning model. The method used to focus on analyzing hidden correlation among the features taken from different characteristic and behaviour patterns of the insured person and remove unimportant noisy features, which are not playing any significant role to increase and decrease the accuracy of risk calculation. Thus, it helps to decrease execution time substantially. This will help Life Insurance Company to take the fast decision in the competitive world. In addition to this, it will protect the company from financial damage by removing noisy features for data mining.

Dimensionality Reduction for Improving the Performance of Risk Calculation Using Machine Learning Algorithms