Inverse Classification Problem of Quantitative Attributes
In order to overcome the disadvantage of most inverse classification algorithms address discrete attributes and can not deal with quantitative attributes. 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. Experimental results on IRIS and Ecoli datasets show that this method could find the class label effectively and estimate the missing values accurately, and the results were not worse than ISGNN and kNN.
A. G. Li et al., "Inverse Classification Problem of Quantitative Attributes", Applied Mechanics and Materials, Vols. 44-47, pp. 3538-3542, 2011