Survey on Feature Selection for Data Reduction

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Survey on Feature Selection for Data Reduction

The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, wh...

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Survey on Feature Selection

Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discu...

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In the present paper, we show that a multi-classification process can be significantly enhanced by selecting an optimal set of the features used as input for the training operation. The selection of such a subset will reduce the dimensionality of the data samples and eliminate the redundancy and ambiguity introduced by some attributes. The used classifier can then operate only on the selected f...

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ژورنال

عنوان ژورنال: International Journal of Computer Applications

سال: 2014

ISSN: 0975-8887

DOI: 10.5120/16456-2390