Extending Sample Information for Small Data Set Prediction
Hung-Yu Chen, Der‐Chiang Li, Liang-Sian Lin · 2016
This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.