TY - JOUR
T1 - A feature selection method for classifying highly similar text documents
AU - Kim, Jeenyoung
AU - Min, Daiki
N1 - Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5A2A03067552)
Publisher Copyright:
© 2021 KIIE.
PY - 2021/6
Y1 - 2021/6
N2 - In the era of big data, the importance of data classification is increasing. However, when it comes to classifying text documents, several obstacles degrade classification performance. These include multi-class documents, high levels of similarity between classes, class size imbalance, high dimensional representation space, and a low frequency of unique and discriminative features. To overcome these obstacles and improve classification performance, this paper proposes a novel feature selection method that effectively utilizes both unique and overlapping features. In general, feature selection methods have ignored unique features that occur only one class because of low frequency while it provides better discriminative-power. On the contrary, overlapping features, which are found in several classes with high frequency, have been also less preferred because of low discriminative-power. The proposed feature selection method attempts to use these two types of features as complementary with aims to improve overall classification performance for highly similar text documents. Extensive numerical analysis have been conducted for three benchmarking datasets with a support vector machine (SVM) classifier. The proposed method showed that not only the class with high similarity but also the general classification performance is superior to the conventional feature selection methods, such as the global feature set, local feature set, discriminative feature set, and information gain.
AB - In the era of big data, the importance of data classification is increasing. However, when it comes to classifying text documents, several obstacles degrade classification performance. These include multi-class documents, high levels of similarity between classes, class size imbalance, high dimensional representation space, and a low frequency of unique and discriminative features. To overcome these obstacles and improve classification performance, this paper proposes a novel feature selection method that effectively utilizes both unique and overlapping features. In general, feature selection methods have ignored unique features that occur only one class because of low frequency while it provides better discriminative-power. On the contrary, overlapping features, which are found in several classes with high frequency, have been also less preferred because of low discriminative-power. The proposed feature selection method attempts to use these two types of features as complementary with aims to improve overall classification performance for highly similar text documents. Extensive numerical analysis have been conducted for three benchmarking datasets with a support vector machine (SVM) classifier. The proposed method showed that not only the class with high similarity but also the general classification performance is superior to the conventional feature selection methods, such as the global feature set, local feature set, discriminative feature set, and information gain.
KW - Feature selection
KW - Hierarchical classification
KW - Multi-classification
KW - Overlapping feature
UR - http://www.scopus.com/inward/record.url?scp=85110301679&partnerID=8YFLogxK
U2 - 10.7232/iems.2021.20.2.148
DO - 10.7232/iems.2021.20.2.148
M3 - Article
AN - SCOPUS:85110301679
SN - 1598-7248
VL - 20
SP - 148
EP - 162
JO - Industrial Engineering and Management Systems
JF - Industrial Engineering and Management Systems
IS - 2
ER -