MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
Description
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED
SUBSPACE CLUSTERING
MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI
Abstract. 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 clustering algorithms.
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MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING |
https://c3.nasa.gov/dashlink/static/media/publication/Paper_22_.pdf |