A hybrid approach of neural network and memory-based learning to data mining

Chung Kwan Shin, Ui Tak Yun, Huy Kang Kim, Sang Chan Park

Research output: Contribution to journalArticle

67 Citations (Scopus)

Abstract

We propose a hybrid prediction system of neural network and memory-based learning. Neural network (NN) and memory-based reasoning (MBR) are frequently applied to data mining with various objectives. They have common advantages over other learning strategies. NN and MBR can be directly applied to classification and regression without additional transformation mechanisms. They also have strength in learning the dynamic behavior of the system over a period of time. Unfortunately, they have shortcomings when applied to data mining tasks. Though the neural network is considered as one of the most powerful and universal predictors, the knowledge representation of NN is unreadable to humans, and this "black box" property restricts the application of NN to data mining problems, which require proper explanations for the prediction. On the other hand, MBR suffers from the feature-weighting problem. When MBR measures the distance between cases, some input features should be treated as more important than other features. Feature weighting should be executed prior to prediction in order to provide the information on the feature importance. In our hybrid system of NN and MBR, the feature weight set, which is calculated from the trained neural network, plays the core role in connecting both learning strategies, and the explanation for prediction can be given by obtaining and presenting the most similar examples from the case base. Moreover, the proposed system has advantages in the typical data mining problems such as scalability to large datasets, high dimensions, and adaptability to dynamic situations. Experimental results show that the hybrid system has a high potential in solving data mining problems.

Original languageEnglish
Pages (from-to)637-646
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume11
Issue number3
DOIs
Publication statusPublished - 2000 May
Externally publishedYes

Keywords

  • Data mining
  • Machine learning
  • Memory-based reasoning
  • Neural network

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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