Abstract
Recent years have been wide efforts in attribute selection research. Attribute selection can efficiently reduce the hypothesis space by removing irrelevant and redundant attributes. Attribute reduction of an information system is a key problem in rough set theory and its applications. In this paper, we compare the performance of attribute selection using two technical tools namely WEKA 3.7 and ROSE2. Filter methods are used an alternative measure instead of the error rate to score a feature subset. This measure was chosen to be fast to compute, at the same time as still capturing the usefulness of the feature set. Many filters provide a feature ranking rather than an explicit best feature subset, and the cutoff point in the ranking is chosen via cross-validation. We used Search methods like Best first and Greedy stepwise to evaluate a subset of features as a group for suitability. We use the internet usage data set for this purpose and then comparison results are tabulated for various methods for searching the solution space to eliminate the irrelevant attribute. Results of this research shows us some minding issues of attribute selection tools where we found better ways to have select irrelevant attributes. Comparing the tools of attributes reductions evidence some considerable different between them. Keywords: Classifications, Data Mining, Rough Set Explorer, Search Methods, Selected Attributes, WEKA
Highlights
Attribute set reduction is essentially a task to remove Βirrelevant and/or redundant features
We describe the data set with various search methods and compare the accuracy obtained by the tools with whole dataset with the selected attributes
The first step of our analysis was to reduce the high data dimensionality[7]. For this purpose we use Rose tool and Weka tool for attribute selection based on various search methods made in the attribute space as shown in Table 1 and Table 2
Summary
Attribute set reduction is essentially a task to remove Βirrelevant and/or redundant features. Irrelevant attributes can be removed without affecting learning performance. The distinction is that a redundant attribute implies the co-presence of another attribute; individually each feature is relevant, but the removal of one of them will not affect learning performance[1]. In this paper we use data mining methods to improve the survey results and in the same hand we are comparing the tools rough set explorer ROSE 2.2 and WEKA. The methods help us to identify attributes which might be classified as highly useful. The parameters describe how effective the internet is It indicates how much a particular student is needed to restrict to use the web sites.
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