Analysis of Subfeature for Classification in Data Mining

Feature selection is a crucial part of data mining applications in the diversity area of data analysis. Most of the feature selection methods issue features analysis for classification, which is not sufficient for classification by feature selection techniques. Classification is involved in traditional classes with redundant feature data, which is not helpful in the database for further analysis. There is a more negligible effect on feature analysis by taking an existing class. So, it proposed to obtain a new class or subclass using the analysis of feature's tiny data named subfeature (SF) data of corresponding instances from a traditional class. These data are involved in the limited number of significant instances where it generates the new class. It is a challenging task to find such instances with subfeatures from any database. Thus, this article proposed the optimization model based on the Lagrangian multiplier to find such data and analyze for a new class from a traditional class. Several algorithms have been taken to explain subfeature data. The theoretical approaches such as domain- and variance-based subfeatures and the convergence of subfeature are used to select subfeatures with the effectiveness of the proposed model. Furthermore, several classifiers with searching methods and statistical methods (i.e., local and global variances) analyze the classification through subfeature data. Result analysis in the experiment on different datasets shows that the proposed model is beneficial toward novel classes based on selected subfeature data.