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논문 기본 정보

자료유형
학술저널
저자정보
Byung-Won Min (Mokwon University) Yong-Sun Oh (Mokwon University)
저널정보
한국콘텐츠학회(IJOC) International JOURNAL OF CONTENTS International JOURNAL OF CONTENTS Vol.15 No.3
발행연도
2019.9
수록면
32 - 38 (7page)

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초록· 키워드

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We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

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ABSTRACT
1. INTRODUCTION
2. CONCEPT OF INCREMENTAL LEARNING MODEL
3. DESIGN AND IMPLEMENTATION
4. PERFORMANCE EVALUATION
5. EXCELLENCE OF THE ACHIEVEMENT
6. CONCLUSION
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