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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.15 No.1
- 발행연도
- 2017.3
- 수록면
- 53 - 61 (9page)
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초록· 키워드
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The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric (F₁).
목차
Abstract
I. INTRODUCTION
II. FEATURE SELECTION METHODS
III. PROPOSED METHOD
IV. CLASSIFICATION ALGORITHMS
V. EXPERIMENTAL WORK
VI. CONCLUSIONS
REFERENCES
참고문헌 (20)
참고문헌 신청
S. Rill, D. Reinel, J. Scheidt, and R. V. Zicari, “PoliTwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis,” Knowledge-Based Systems, vol. 69, pp. 24-33, 2014.
A. S. Ghareb, A. B. Bakar, and A. R. Hamdan, “Hybrid feature selection based on enhanced genetic algorithm for text categorization,” Expert Systems with Applications, vol. 49, pp. 31- 47, 2016.
H. Elghazel, A. Aussem, O. Gharroudi, and W. Saadaoui, “Ensemble multi-label text categorization based on rotation forest and latent semantic indexing,” Expert Systems with Applications, vol. 57, pp. 1-11, 2016.
Y. Wang, Y. Liu, L. Feng, and X. Zhu, “Novel feature selection method based on harmony search for email classification,” Knowledge-Based Systems, vol. 73, pp. 311-323, 2015.
J. Yang, Z. Liu, and Z. Qu, “A novel feature selection based gravitation for text categorization,” International Journal of Database Theory and Application, vol. 9, pp. 211-228, 2016.
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