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You searched for id:"oai:NSYSU:etd-0802117-142741". One record found.

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NSYSU

1. Lai , Jyun-Hao. Ensemble Learning for Text Classiï¬cation.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

The text classification (text categorization, document classification, or document categorization) problem is to assign a given document to one of the predefined classes. The problem has been studied in many fields, such as library science, information science and computer science. Though several studies were devoted to the text classification problem, few of them discussed the Chinese text classification. In this thesis, we study the Chinese text classification problem. Since there is no public dataset for our problem, our experimental dataset was downloaded from the yahoo news web site. The dataset consists of about the 50,000 Chinese news articles in 9 classes. We constitute these news documents into five types of sources: (1) full text, (2) title, (3) first paragraph, (4) full text and title, and (5) title and first paragraph. We use three feature generation methods (TF-IDF, Ï2 and IG) to produce the feature vector from each document. We adopt the SVM method as our basic classifier, thus 15 SVM classifiers are trained. Next, we choose any three of them to constitute an ensemble classifier by the BKS method, so totally (15¦3)=455 ensemble classifiers are constructed. The experimental results show that our suggestion ensemble classifier formed by TF-IDF(full text and title), Ï2(title) and IG(title) has good prediction accuracy 79.04%. Advisors/Committee Members: Chang-Biau Yang (committee member), Sun-Yuan Hsieh (chair), Chia-Ping Chen (chair), Tzung-Pei Hong (chair), Kuo-Tsung Tseng (chair).

Subjects/Keywords: support vector machine (SVM); ensemble learning; behavior knowledge space (BKS); Chinese text classification; feature generation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lai , J. (2017). Ensemble Learning for Text Classiï¬cation. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0802117-142741

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lai , Jyun-Hao. “Ensemble Learning for Text Classiï¬cation.” 2017. Thesis, NSYSU. Accessed November 17, 2017. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0802117-142741.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lai , Jyun-Hao. “Ensemble Learning for Text Classiï¬cation.” 2017. Web. 17 Nov 2017.

Vancouver:

Lai J. Ensemble Learning for Text Classiï¬cation. [Internet] [Thesis]. NSYSU; 2017. [cited 2017 Nov 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0802117-142741.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lai J. Ensemble Learning for Text Classiï¬cation. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0802117-142741

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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