A New Fuzzy Supervised Classification Method Based on Aggregation Operator
A New Fuzzy Supervised Classification Method Based on Aggregation Operator
复制标题
一种基于聚合算子的模糊监督分类新方法
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
S. Meher
中科院分区：
文献类型：
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作者：
S. Meher
提出了一种基于集结算子的模糊监督分类方法。建议的分类器聚合的信息，通过探索属于类的特征明智的程度。我使用了一个pi型隶属函数和MEAN（平均）聚集推理规则（算子）。该分类器的有效性进行了验证与四个基准数据集，包括实时金融领域的数据。各种性能指标用于定量评估的分类器。这些数据集上的实验结果表明，显式模糊，模糊k-最近邻和模糊最大似然的其他三个模糊分类器相比，该方法的分类性能显着改善。
A new fuzzy supervised classification method based on aggregation operator is proposed in the present article. The proposed classifier aggregates the information extracted by exploring feature-wise degree of belonging to classes. I uses a pi-type membership function and MEAN (average) aggregation reasoning rule (operator). The effectiveness of the proposed classifier is verified with four benchmark data sets including a realtime financial domain data. Various performance measures are used for quantitative evaluation of the classifier. Experimental results on these data sets illustrate significant improvement in the classification performance of the proposed method compared to three other fuzzy classifiers, namely, explicit fuzzy, fuzzy k-nearest neighbor and fuzzy maximum likelihood.