Earticle

초록

영어

Text classification is a challenging task, especially when dealing with a huge amount of text data. The performance of a classification model can be varied depending on what type of words contained in the document corpus and what type of features generated for classification. Aside from proposing a new modified version of the existing algorithm or creating a new algorithm, we attempt to modify the use of data. The classifier performance is usually affected by the quality of learning data as the classifier is built based on these training data. We assume that the data from different domains might have different characteristics of noise, which can be utilized in the process of learning the classifier. Therefore, we attempt to enhance the robustness of the classifier by injecting the heterogeneous data artificially into the learning process in order to improve the classification accuracy. Semi-supervised approach was applied for utilizing the heterogeneous data in the process of learning the document classifier. However, the performance of document classifier might be degraded by the unlabeled data. Therefore, we further proposed an algorithm to extract only the documents that contribute to the accuracy improvement of the classifier.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Work
2.1. Data Heterogeneity and Robustness
2.2. Semi-Supervised Learning
2.3. Ensemble Learning
Ⅲ. Research Methodology
3.1. Research Overview
3.2. Module 1: Heterogeneity Injection
3.3. Module 2: Classification Rule Selection
Ⅳ. Data Analysis and Results
4.1. Data Description
4.2. Data Preparation
4.3. Experiments and Results
Ⅴ. Conclusion

저자

• William Xiu Shun Wong [ Senior Consultant, Biz Consulting Team, Datasolution Inc., Korea ]
• Donghoon Lee [ Staff, BI LAB, Cafe24 Corp., Korea ]
• Namgyu Kim [ Professor, School of Management Information Systems, Kookmin University, Korea ] Corresponding author

참고문헌