Improving Classification Accuracy Using Missing Data Filling Algorithms for the Criminal Dataset

Abstract

Predicting crime types by using classification algorithms can help to find factors affecting crimes and prevent crimes. Due to various reasons in the process of data collection, there are often a large number of missing values in actual criminal dataset, which seriously affects the classification accuracy. Therefore, based on mutual KNNI (K nearest neighbor imputation) algorithm and combined with GRA (Grey Relational Analysis) theory, a novel data filling algorithm called GMKNN is proposed in order to improve the classification accuracy. The algorithm replaces the Euclidean distance formula used in mutual KNNI algorithm with the Grey relational grade formula to eliminate the effect of noise from the nearest neighbors and effectively deal with the discrete attributes. By comparing with several popular data filling algorithms based on a real criminal dataset with lots of missing values, higher classification accuracy can be obtained by using GMKNN algorithm, which is up to 77.837%.