A Novel Feature Selection and Extraction Technique for Classification
This addresses the curse of dimensionality for researchers and practitioners in machine learning, though it appears incremental as it builds on existing feature selection and extraction methods.
The paper tackled the problem of high dimensionality in classification by introducing Class Dependent Features (CDFs) for feature selection and extraction, resulting in improved accuracy and reduced computational expense, as demonstrated on handwritten digit recognition and text categorization tasks.
This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to handwritten digit recognition and text categorization.