.png){kind=icon}
返回
An instance-based learning approach based on grey relational structure
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-006-0105-0.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
instance-based learning
grey relational analysis
grey relational structure
pattern classification
期刊
IF:
3.5
论文数:
7.7K
被引数:
1.7W
机构
暂无机构信息
引用论文
Automatic growing of a hopfield style network during training for classification.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏分享收藏分享收藏Heavy metals research in Nigeria: a review of studies and prioritization of research needs尼日利亚的重金属研究: 研究回顾和研究需求的优先次序
OAAI
OAAI分享收藏分享收藏
分享收藏
查看更多内容