An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification
Pengelasan teks automatik adalah penting kerana peningkatan bilangan dokumen digital dan oleh itu ia perlu diurus. Kaedah pemodelan statistik terkini tidak memberi maklumat berguna yang mencukupi tentang topik untuk setiap ciri dan kategori. Tambahan pula, penyarian sifat menggunakan frekuensi kata-...
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| Format: | Thesis |
| Language: | English |
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2016
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| Online Access: | http://eprints.usm.my/31479/ http://eprints.usm.my/31479/1/AMMAR_ISMAEL_KADHIM_24.pdf |
| _version_ | 1848876581584371712 |
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| author | Kadhim, Ammar Ismael |
| author_facet | Kadhim, Ammar Ismael |
| author_sort | Kadhim, Ammar Ismael |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Pengelasan teks automatik adalah penting kerana peningkatan bilangan dokumen digital dan oleh itu ia perlu diurus. Kaedah pemodelan statistik terkini tidak memberi maklumat berguna yang mencukupi tentang topik untuk setiap ciri dan kategori. Tambahan pula, penyarian sifat menggunakan frekuensi kata-frekuensi dokumen songsang (TF-IDF) tradisional menghasilkan pengenalan kategori yang terlalu banyak untuk sesuatu dokumen. Dalam usaha pengelasan pula, kaedah k-jiran terdekat (k-NN) sedia ada dengan jarak Euclid dan skor keserupaan kosinus menghasilkan julat varians yang besar dalam prestasinya. Untuk menangani isu ini, kajian ini mengelaskan topik untuk teks pendek dan panjang dengan menggunakan pendekatan baharu untuk tahap-tahap utama pengelasan teks (iaitu penyarian sifat dan pengelasan teks). Kajian ini juga memperkenalkan TD-IDF dengan logaritma dan k-NN dengan skor keserupaan kosinus yang baharu untuk penyarian sifat dan pengelasan masing-masing. Lagipun, faktor yang memberi kesan terhadap prestasi pembelajaran mesin berselia juga dikenalpasti.
Automatic text classification is important because of the increased availability of digital documents and therefore the need to organize them. The current state-of-the-art statistical modeling approaches do not provide sufficient useful information on the topics for each feature and category. Furthermore, feature extraction using traditional term frequency-inverse document frequency (TF-IDF) results in the identification of too many categories for a particular document. In terms of classification, current k-NN approaches with Euclidean distance and cosine similarity score produce a wide range of variance in performance. To address these issues, this study classifies topics for short and long texts using a new method for the main stage (i.e., feature extraction and text classification). The study also introduces TF-IDF with logarithm and k-NN with a new cosine similarity score for feature extraction and classification, respectively. |
| first_indexed | 2025-11-15T17:01:49Z |
| format | Thesis |
| id | usm-31479 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:01:49Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-314792019-04-12T05:25:22Z http://eprints.usm.my/31479/ An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification Kadhim, Ammar Ismael QA75.5-76.95 Electronic computers. Computer science Pengelasan teks automatik adalah penting kerana peningkatan bilangan dokumen digital dan oleh itu ia perlu diurus. Kaedah pemodelan statistik terkini tidak memberi maklumat berguna yang mencukupi tentang topik untuk setiap ciri dan kategori. Tambahan pula, penyarian sifat menggunakan frekuensi kata-frekuensi dokumen songsang (TF-IDF) tradisional menghasilkan pengenalan kategori yang terlalu banyak untuk sesuatu dokumen. Dalam usaha pengelasan pula, kaedah k-jiran terdekat (k-NN) sedia ada dengan jarak Euclid dan skor keserupaan kosinus menghasilkan julat varians yang besar dalam prestasinya. Untuk menangani isu ini, kajian ini mengelaskan topik untuk teks pendek dan panjang dengan menggunakan pendekatan baharu untuk tahap-tahap utama pengelasan teks (iaitu penyarian sifat dan pengelasan teks). Kajian ini juga memperkenalkan TD-IDF dengan logaritma dan k-NN dengan skor keserupaan kosinus yang baharu untuk penyarian sifat dan pengelasan masing-masing. Lagipun, faktor yang memberi kesan terhadap prestasi pembelajaran mesin berselia juga dikenalpasti. Automatic text classification is important because of the increased availability of digital documents and therefore the need to organize them. The current state-of-the-art statistical modeling approaches do not provide sufficient useful information on the topics for each feature and category. Furthermore, feature extraction using traditional term frequency-inverse document frequency (TF-IDF) results in the identification of too many categories for a particular document. In terms of classification, current k-NN approaches with Euclidean distance and cosine similarity score produce a wide range of variance in performance. To address these issues, this study classifies topics for short and long texts using a new method for the main stage (i.e., feature extraction and text classification). The study also introduces TF-IDF with logarithm and k-NN with a new cosine similarity score for feature extraction and classification, respectively. 2016-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/31479/1/AMMAR_ISMAEL_KADHIM_24.pdf Kadhim, Ammar Ismael (2016) An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Kadhim, Ammar Ismael An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title | An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title_full | An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title_fullStr | An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title_full_unstemmed | An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title_short | An Improved K-Nearest Neighbors Approach Using Modified Term Weighting And Similarity Coefficient For Text Classification |
| title_sort | improved k-nearest neighbors approach using modified term weighting and similarity coefficient for text classification |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/31479/ http://eprints.usm.my/31479/1/AMMAR_ISMAEL_KADHIM_24.pdf |