On improving the classification accuracy of extension theory

Extension theory (ET) has been criticized mainly because of the following: (1) requirement of specifying the classical domain by user-experience; (2) lack of providing useful summaries of asymmetric data; and (3) failure to implement data classification when attributes are categorical. This paper proposes two well-motivated modifications over the ET framework, called mode-based extension theory (mbET) and frequency-based extension theory (FET), which alleviate the three difficulties of traditional extension theory. Experiment results on 13 widely used UCI data sets show that mbET and FET can achieve superior classification accuracy compared to ET. The FET achieved the most outstanding improvement over ET in classification accuracy, which is statistically significant. According to the experiment results, FET was also found to be superior or comparable to other state-of-the-art classifiers. The experimental results also encourage the application of FET to more real-world classification problems.