AFE-MERT: imbalanced text classification with abstract feature extraction
Yazarlar (2)
Doç. Dr. Murat OKKALIOĞLU Yalova Üniversitesi, Türkiye
Dr. Öğr. Üyesi Burcu DEMİRELLİ OKKALIOĞLU Yalova Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Applied Intelligence (Q2) | ||
| Dergi ISSN | 0924-669X Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2022 |
| Cilt / Sayı / Sayfa | 52 / 9 / 10352–10368 | DOI | 10.1007/s10489-021-02983-2 |
| Makale Linki | http://dx.doi.org/10.1007/s10489-021-02983-2 | ||
| UAK Araştırma Alanları |
Bilgisayar Bilimleri ve Mühendisliği
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| Özet |
| The class imbalance problem occurs when the distribution among classes is not balanced. This can be a problem that causes classifier models to bias toward classes with many training samples. The class imbalance problem is inherent in text classification. The abstract feature extraction method is a versatile term weighting scheme. It serves not only as a feature extractor to form a structural form from unorganized text data but also as a dimension reduction technique and classifier. In this study, we tackle the problem of class imbalance in abstract feature extraction. The proposed method utilizes relative imbalance ratio as a factor to elevate the representation of minority classes. Besides, we also integrate relevant term factors to boost the general accuracy. Experiments conducted with three different data sets, one of which is collected for this study, show that the original abstract feature extraction method indeed … |
| Anahtar Kelimeler |
| Abstract feature extraction | Class imbalance | Relative imbalance ratio | Relevant term | Term weighting |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 7 |
| Scopus | 14 |
| Google Scholar | 20 |