In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analy...

The Clustering is a method of grouping the information into modules or clusters. Their dimensionality increases usually with a tiny number of dimensions that are significant to definite clusters, but data in the unrelated dimensions may produce much noise and wrap the actual clusters to be exposed. Attribute subset selection method is frequently used for data reduction through removing unrelate...

different approaches have been proposed for feature selection to obtain suitable features subset among all features. these methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. the objective functions are divided into two main groups: filter and wrapper methods.  in filter methods, features subsets are selected due to some measu...

Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. It is of the greatest importance to take the most of every evaluation of the inducer, which is normally the more costly part. In this paper, a technique is proposed that takes into account the inducer evaluation both in the ...

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbi...

In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbi...

In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbi...

A Feature selection algorithm employ for removing irrelevant, redundant information from the data. Amongst feature subset selection algorithm filter methods are used because of its generality and are usually good choice when numbers of features are large. In cluster analysis, graph-theoretic clustering methods to features are used. In particular, the minimum spanning tree (MST)based clustering ...

Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the q...