Optimizing Data Transformation for Binary Classification
Kangrok Oh, Kar‐Ann Toh, Zhengguo G. Li · International Journal of Computer Theory and Engineering · 2017
In this paper, we propose to optimize a data transformation matrix and study its impact on binary classification.Based on the area above the receiver operating characteristics curve (AAC) minimization with data transformation, we optimize alternatingly between the data transformation matrix and the weighting parameter vector.Some experimental results on 16 binary data sets acquired from the UCI machine learning repository are observed and discussed.Classification accuracy and ranking value averaged from 10 runs of stratified 10-fold cross-validation are adopted as performance indicators.The proposed method shows encouraging results based on these two performance indicators.In addition, it is shown that most of the performance comparisons are statistically significant.