Unsupervised Feature Weighting Based on Local Feature Relatedness

From Wikipedia Quality
Jump to: navigation, search
Unsupervised Feature Weighting Based on Local Feature Relatedness
Authors
Jiali Yun
Liping Jing
Jian Yu
Houkuan Huang
Publication date
2011
ISSN
03029743
ISBN
978-364220840-9
DOI
10.1007/978-3-642-20841-6-4
Links

Unsupervised Feature Weighting Based on Local Feature Relatedness - scientific work about Wikipedia quality published in 2011, written by Jiali Yun, Liping Jing, Jian Yu and Houkuan Huang.

Overview

Feature weighting plays an important role in text clustering. Traditional feature weighting is determined by the syntactic relationship between feature and document (e.g. TF-IDF). In this paper, a semantically enriched feature weighting approach is proposed by introducing the semantic relationship between feature and document, which is implemented by taking account of the local feature relatedness - the relatedness between feature and its contextual features within each individual document. Feature relatedness is measured by two methods, document collection-based implicit relatedness measure and Wikipedia link-based explicit relatedness measure. Experimental results on benchmark data sets show that the new feature weighting approach surpasses traditional syntactic feature weighting. Moreover, clustering quality can be further improved by linearly combining the syntactic and semantic factors. The new feature weighting approach is also compared with two existing feature relatedness-based approaches which consider the global feature relatedness (feature relatedness in the entire feature space) and the inter-document feature relatedness (feature relatedness between different documents) respectively. In the experiments, the new feature weighting approach outperforms these two related work in clustering quality and costs much less computational complexity.

Embed

Wikipedia Quality

Yun, Jiali; Jing, Liping; Yu, Jian; Huang, Houkuan. (2011). "[[Unsupervised Feature Weighting Based on Local Feature Relatedness]]". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6634 LNAI, Issue PART 1, 2011, pp. 38-49. ISBN: 978-364220840-9. ISSN: 03029743. DOI: 10.1007/978-3-642-20841-6-4.

English Wikipedia

{{cite journal |last1=Yun |first1=Jiali |last2=Jing |first2=Liping |last3=Yu |first3=Jian |last4=Huang |first4=Houkuan |title=Unsupervised Feature Weighting Based on Local Feature Relatedness |date=2011 |isbn=978-364220840-9 |issn=03029743 |doi=10.1007/978-3-642-20841-6-4 |url=https://wikipediaquality.com/wiki/Unsupervised_Feature_Weighting_Based_on_Local_Feature_Relatedness |journal=Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6634 LNAI, Issue PART 1, 2011, pp. 38-49}}

HTML

Yun, Jiali; Jing, Liping; Yu, Jian; Huang, Houkuan. (2011). &quot;<a href="https://wikipediaquality.com/wiki/Unsupervised_Feature_Weighting_Based_on_Local_Feature_Relatedness">Unsupervised Feature Weighting Based on Local Feature Relatedness</a>&quot;. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6634 LNAI, Issue PART 1, 2011, pp. 38-49. ISBN: 978-364220840-9. ISSN: 03029743. DOI: 10.1007/978-3-642-20841-6-4.