Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature selection procedure, constructing proper FSAs to each given dataset. Here is discussed constructive metalevel feature selection that re-constructs proper FSAs with a method repository every given datasets, de-composing representative FSAs into methods. After implementing the constructive meta-level feature selection system, we show how constructive meta-level feature selection goes well with 34 UCI common data sets, comparing with typical FSAs on their accuracies. As the result, our system shows the high performance on accuracies with lower computational costs to construct a proper FSA to each given data set automatically.
ASJC Scopus subject areas
- Computer Science(all)