An instance-based learning approach based on grey relational structure

摘要

In instance-based learning, the ‘nearness’ between two instances—used for pattern classification—is generally determined by some similarity functions, such as the Euclidean or Value Difference Metric (VDM). However, Euclidean-like similarity functions are normally only suitable for domains with numeric attributes. The VDM metrics are mainly applicable to domains with symbolic attributes, and their complexity increases with the number of classes in a specific application domain. This paper proposes an instance-based learning approach to alleviate these shortcomings. Grey relational analysis is used to precisely describe the entire relational structure of all instances in a specific domain. By using the grey relational structure, new instances can be classified with high accuracy. Moreover, the total number of classes in a specific domain does not affect the complexity of the proposed approach. Forty classification problems are used for performance comparison. Experimental results show that the proposed approach yields higher performance over other methods that adopt one of the above similarity functions or both. Meanwhile, the proposed method can yield higher performance, compared to some other classification algorithms.