Performance Analysis of Quantitative Attributes Inverse Classification Problem

Most inverse classification algorithms address discrete attributes and can not deal with quantitative attributes. In order to overcome the disadvantage, the discretization algorithms are applied to the inverse classification algorithms, and the main idea is: firstly, a group of feature attributes are selected by using feature selection algorithm; then, the quantitative attributes are discretized by using discretization algorithms, and the inverted statistics are constructed on the training samples; finally, the test samples are analyzed in order to classify and estimate the missing values. Experimental results on IRIS and Ecoli datasets show that this method could find the class label effectively and estimate the missing values accurately. The performance of the equal-width histogram method is better in the inverse classification problem of quantitative attributes.