Title :
Utilizing Category Relevancy Factor for text categorization
Author_Institution :
Iran Telecommun. Res. Center, Tehran, Iran
Abstract :
One of the main preprocessing steps for having a high performance text classifier is feature weighting. Commonly used feature weighting methods such as TF and IDF-based methods only consider the distribution of a feature in the document(s) and do not consider class information for feature weighting. In this paper, we present TFCRF (Term Frequency and Category Relevancy Factor) method in which the weight of features depends on their power to discriminate the classes from each other by using class information. The results show significant improvement in the performance of SVM algorithm by using TFCRF feature weighting method in comparison to the other implemented standard feature weighting methods.
Keywords :
data mining; learning (artificial intelligence); pattern classification; support vector machines; text analysis; SVM algorithm; class information; feature weighting; term frequency and category relevancy factor; text categorization; text classifier; Computational modeling; Frequency; Information retrieval; Kernel; Standards development; Support vector machine classification; Support vector machines; Text categorization; Text mining; Tuning; Feature weighting; SVM; Text categorization; Text mining;
Conference_Titel :
Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-7324-3
Electronic_ISBN :
978-89-88678-22-0
