Results 221 to 230 of about 146,160 (266)

An algorithm for decision tree and attribute reduction

2012 International Conference on Machine Learning and Cybernetics, 2012
A decision tree and an attribute reduct from the same crisp decision table are often obtained respectively with different algorithms. Developing an algorithm for both of them is theoretically important and practically useful. This paper proposes an algorithm generating both decision tree and attribute reduction from a crisp decision table.
Qun-Feng Zhang, Yu-Fen Zhang, Ming-Zhu Hao   +2 more
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Local Search for Attribute Reduction

2019
Two new attribute reduction algorithms based on iterated local search and rough sets are proposed. Both algorithms start with a greedy construction of a relative reduct. Then attempts to remove some attributes to make the reduct smaller. Process of attributes selection is the main difference between the algorithms. It is random for the first one, and a
Xiaojun Xie, Ryszard Janicki, Xiaolin Qin, Wei Zhao 0061, Guangmei Huang   +4 more
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Attribute reduction for imprecise decision tables

2012 IEEE International Conference on Granular Computing, 2012
In this paper, we investigate approaches to attribute reduction for decision tables with imprecise decision attribute values. First, we introduce imprecise decision tables, and presumable and possible decision attribute value sets. We define several meaningful object sets based on the twofold decision attribute value sets.
Masahiro Inuiguchi, Bingjun Li
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Consistency Based Attribute Reduction

2007
Rough sets are widely used in feature subset selection and attribute reduction. In most of the existing algorithms, the dependency function is employed to evaluate the quality of a feature subset. The disadvantages of using dependency are discussed in this paper. And the problem of forward greedy search algorithm based on dependency is presented.
Qinghua Hu, Hui Zhao, Zongxia Xie, Daren Yu   +3 more
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Multi-granularity Attribute Reduction

2018
It is known that different parameters used in Gaussian kernel will provide us different granularities of information granulations. Therefore, kernel based fuzzy rough set has the characteristic of multi-granularity. From this point of view, a multi-granularity attribute reduction strategy is developed in this paper. Different from traditional reduction
Shaochen Liang, Keyu Liu, Xiangjian Chen, Pingxin Wang, Xibei Yang   +4 more
openaire   +1 more source

Test-cost-sensitive attribute reduction

Information Sciences, 2011
In many data mining and machine learning applications, there are two objectives in the task of classification; one is decreasing the test cost, the other is improving the classification accuracy. Most existing research work focuses on the latter, with attribute reduction serving as an optional pre-processing stage to remove redundant attributes.
Fan Min 0001, Huaping He, Yuhua Qian, William Zhu 0001   +3 more
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Research on attribute reduction algorithm with weights

Journal of Intelligent & Fuzzy Systems, 2014
Rough set is a mathematical tool proposed by professor Pawlak to deal with uncertain knowledge. Attribute reduction is one of the core contents of rough set theory when people acquire knowledge from an information system. The existing reduction algorithms are often based on a kind of attribute importance, without considering the application information
Qinghua Zhang 0001, Wen Shen 0004
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Reducts and constructs in attribute reduction

Fundam. Informaticae, 2004
Summary: One of the main notions in the Rough Set Theory (RST) is that of a reduct. According to its classic definition, the reduct is a minimal subset of the attributes that retains some important properties of the whole set of attributes. The idea of the reduct proved to be interesting enough to inspire a great deal of research and resulted in ...
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Accelerator for multi-granularity attribute reduction

Knowledge-Based Systems, 2019
Abstract By considering the information granulation in Granular Computing, the concept of the multi-granularity is important. It is mainly because different results of information granulation will imply different levels of granularity. Nevertheless, multi-granularity has been paid less attention to the problem of attribute reduction in rough set ...
Zehua Jiang, Xibei Yang, Hualong Yu, Dun Liu, Pingxin Wang, Yuhua Qian   +5 more
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Attribute Reduction Based on Continuous Attribute Domain

Advanced Materials Research, 2014
Discrete data attributes reduction, there are many mature methods, but for continuous data attributes reduction, general algorithm is not very good, in real life, the continuous data feature extraction and discrete data is also important, based on the number of new brain waves as analysis object, and through the continuous eeg feature extraction ...
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