Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Classification of Tabular Data using High-Dimensional Mapping and Deep Learning Network

고차원 매핑기법과 딥러닝 네트워크를 통한 정형데이터의 분류

  • Kyeong-Taek Kim (Artificial Intelligence Convergence, Pukyong National University) ;
  • Won-Du Chang (Division of Computer Engineering and Artificial Intelligence, Pukyong National University)
  • 김경택 (부경대학교 인공지능융합학과) ;
  • 장원두 (부경대학교 컴퓨터.인공지능공학부)
  • Received : 2023.10.26
  • Accepted : 2023.11.24
  • Published : 2023.12.31
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Abstract

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

최근 딥러닝은 다양한 분야에서 전통적인 기계학습에 비해 월등히 높은 성능을 보이고 있으며, 패턴인식을 위한 보편적인 방법으로 자리 잡아 가고 있다. 하지만, 이에 비해 정형데이터를 사용하는 분류 문제에서는 여전히 머신러닝 기법이 주류를 이루고 있다. 본 논문에서는 정형데이터를 고차원 텐서로 변환하는 네트워크 모듈을 제안하며, 이 모듈을 보편적인 딥러닝 네트워크와 함께 구성하여 정형데이터의 분류 문제에 적용하였다. 제안된 방법은 4종의 데이터셋을 활용하여 학습 및 검증되었으며, 제안된 방법은 90.22%의 평균 정확도를 달성하여, 최신 딥러닝 모델인 TabNet에 비해 2.55%p 높은 정확도를 보였다. 제안된 방법은 컴퓨터 비전 분야에서 높은 성능을 보이는 다양한 네트워크 구조를 정형데이터에 활용할 수 있다는 점에서 의미가 있다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 지역지능화혁신인재양성(Grand ICT연구센터) 사업 (IITP-2023-2016-0-00318)과 과학기술정보통신부의 재원으로 수행된 연구개발특구진흥재단-기술사업화 역량강화 사업의 지원을 받아 수행된 연구임.(No. 2023-BS-RD-0061 / 지능형 보안 감시 시스템 고도화 및 상용화 기술 개발)

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