Categorical Proportional Difference: A Feature Selection Method for Text Categorization

Supervised text categorization is a machine learning task where a predefined category label is automatically assigned to a previously unlabelled document based upon characteristics of the words contained in the document. Since the number of unique words in a learning task (i.e., the number of features) can be very large, the efficiency and accuracy of the learning task can be increased by using feature selection methods to extract from a document a subset of the features that are considered most relevant. In this paper, we introduce a new feature selection method called categorical proportional difference (CPD), a measure of the degree to which a word contributes to differentiating a particular category from other categories. The CPD for a word in a particular category in a text corpus is a ratio that considers the number of documents of a category in which the word occurs and the number of documents from other categories in which the word also occurs. We conducted a series of experim...

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Mondelle Simeon, Robert J. Hilderman
• Keywords: AUSDM 2008, Data Mining, Document, Feature Selection Methods, Learning Task