Cost-sensitive decision trees usually concern the discussion of the test cost and misclassification cost. During the classification process, splitting attribute selection is the most important. The paper analyzed the disadvantages and the advantages of the existing methods and proposed a novel method that combined the information ratio in information theory with the cost including the test cost and the misclassification cost to select the split attributes. The experimental results show that this method outperforms significantly the existing methods.

The Euclidean-based k-Nearest Neighbor (kNN) algorithm is restricted to the dataset without correlation-sensitive on density. The author proposed an improved kNN algorithm based on Mahalanobis distance and gray analysis for imputing missing data to replace the existing Euclidean distance. The Mahalanobis distances can deal with the issue of correlation-sensitive on density, and the gray-analysis method can deal with the opposite case. Hence, the proposed method can deal with any kind of datasets, and the experimental results show the proposed method outperforms the existing algorithms.