Multi-label ASRS Dataset Classification Using Semi Supervised Subspace Clustering

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data - multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data. In this paper, we propose a text classification technique that considers this characteristic and provides very good performance. Our multi-label text classification approach is an extension of our previously formulated [3] multi-class text classification approach called SISC (Semi-supervised Impurity based Subspace Clustering). We call this new classification model as SISC-ML(SISC Multi-Label). Empirical evaluation on real world multi-label NASA ASRS (Aviation Safety Reporting System) data set reveals that our approach outperforms state-of-theart text classification as well as subspace cluster...

• Type: Journal
• Year: 2010
• Where: CIDU
• Authors: Mohammad Salim Ahmed, Latifur Khan, Nikunj C. Oza, Mandava Rajeswari
• Keywords: CIDU 2010, Classification Technique, Machine Learning, Text Classification, Text Classification Approach