Improved Feature Weight Algorithm and Its Application to Text Classification

Songtao Shang, Minyong Shi, Wenqian Shang and Zhiguo Hong

Mathematical Problems in Engineering, 2016, vol. 2016, 1-12

Abstract:

Text preprocessing is one of the key problems in pattern recognition and plays an important role in the process of text classification. Text preprocessing has two pivotal steps: feature selection and feature weighting. The preprocessing results can directly affect the classifiers’ accuracy and performance. Therefore, choosing the appropriate algorithm for feature selection and feature weighting to preprocess the document can greatly improve the performance of classifiers. According to the Gini Index theory, this paper proposes an Improved Gini Index algorithm. This algorithm constructs a new feature selection and feature weighting function. The experimental results show that this algorithm can improve the classifiers’ performance effectively. At the same time, this algorithm is applied to a sensitive information identification system and has achieved a good result. The algorithm’s precision and recall are higher than those of traditional ones. It can identify sensitive information on the Internet effectively.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2016/7819626.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2016/7819626.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7819626

DOI: 10.1155/2016/7819626

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().