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Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion‐based classification from...
Without sufficient prior knowledge the identification of the optimal cluster numbers is a difficult problem for unsupervised clustering. Since fuzzy entropy is essential for measuring the information of fuzzy sets, a combined fuzzy entropy index (CFE) is developed for searching the best number of clusters kb. The CFE involves the compactness and the separation of clusters both in the data space and...
In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy...
The advantages of both grey clustering method and fuzzy ISODATA method were analyzed and colligated. First, the decision-making evaluation results of uncertainty system with small sample were acquired with grey clustering method. Then, applying the fuzzy ISODATA model to learn and revise the results of above grey clustering, an optimal fuzzy classification could be produced through iterative operation...
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