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Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets...
Automated Modulation Classification (AMC) shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. A useful digital signal modulation recognition scheme inspired by the deep auto-encoder network is proposed in this investigation. In our proposed method, there are two deep auto-encoder networks. The system extracts the original features of...
Many real world classification problems lack of a large number of labeled data for learning an effective classifier. Active learning methods seek to address this problem by reducing the number of labeled instances needed to build an effective classifier. Most current active learning methods, however, are myopic, i.e. select one single unlabelled sample to label at a time. Obviously, such a strategy...
This paper improves CNM algorithm to detect community structure on weighted network. Based on the link weight and vertex weight, algorithm design defines a new Q-function to calculate community modularity, the type of communities were classified by finding the Q peak. We have generated networks with known community structure A,B and C(different sizes), to test if the algorithms can recognize and extract...
Tree similarity measurement is key to tree-like data mining. In order to maximally capture common information between trees, we consider the problem of computing all common embedded subtrees, and advocate using the number/count of all common embedded subtrees as a measure of similarity. This problem is not trivial due to the inherent complexity of trees and the ensued large search space. The problem...
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