Classification Based on both Attribute Value Weight and Tuple Weight under the Cloud Computing

Yifeng Zheng, Zaixiang Huang and Tianzhong He

Mathematical Problems in Engineering, 2013, vol. 2013, 1-7

Abstract:

In recent years, more and more people pay attention to cloud computing. Users need to deal with magnanimity data in the cloud computing environment. Classification can predict the need of users from large data in the cloud computing environment. Some traditional classification methods frequently adopt the following two ways. One way is to remove instance after it is covered by a rule, another way is to decrease tuple weight of instance after it is covered by a rule. The quality of these traditional classifiers may be not high. As a result, they cannot achieve high classification accuracy in some data. In this paper, we present a new classification approach, called classification based on both attribute value weight and tuple weight (CATW). CATW is distinguished from some traditional classifiers in two aspects. First, CATW uses both attribute value weight and tuple weight. Second, CATW proposes a new measure to select best attribute values and generate high quality classification rule set. Our experimental results indicate that CATW can achieve higher classification accuracy than some traditional classifiers.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:436368

DOI: 10.1155/2013/436368

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