Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with pertinent features would reduce the size of dataset and afterwards allow more apparent analysis of extracted rules pattern and rules. This pap...

In this article a dense subgraph finding approach is adopted for the unsupervised feature selection problem. The feature set of a data is mapped to a graph representation with individual features constituting the vertex set and inter-feature mutual information denoting the edge weights. Feature selection is performed in a two-phase approach where the densest subgraph is first obtained so that t...

Objective: In processing high dimensional clinical data, choosing the optimal subset of features is important, not only for reduce the computational complexity but also to improve the value of the model constructed from the given data. This study proposes an efficient feature selection method with a variable threshold. Methods: In the proposed method, the spatial distribution of labeled data, w...

MOTIVATION Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of th...

In this study, we propose a novel way to describe the variety of environmental adaptations of Archaea. We have clustered 57 Archaea by using a non-redundant set of proteomic features, and verified that the clusters correspond to environmental adaptations to the archaeal habitats. The first cluster consists dominantly of hyperthermophiles and hyperthermoacidophilic aerobes. The second cluster jo...

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of tra...

Brain tumors are the second leading cause of cancer deaths in human throughout the world. Therefore accurate diagnosis is important for successful treatment of brain tumor. When modeling a complex, poorly defined problem with hundreds of possible inputs one must identify the significant inputs before any known modeling techniques can be applied. As generally the data contains many redundant fea...

People are especially efficient in processing certain visual stimuli such as human faces or good configurations. It has been suggested that topology and geometry play important roles in configural perception. Visual search is one area in which configurality seems to matter. When either of 2 target features leads to a correct response and the sequence includes trials in which either or both targ...

We analyse sequence and structural features of protein-RNA interfaces using RB-147, a non-redundant dataset of protein-RNA complexes extracted from the PDB. We train classifiers using machine learning algorithms to predict protein-RNA interfaces from sequence and structure-derived features of proteins. Our experiments show that Struct-NB, a Naive Bayes classifier that exploits structural featur...

Many indices for evaluation of features have been considered. Applied to single features they allow for filtering irrelevant attributes. Algorithms for selection of subsets of features also remove redundant features. Hashing techniques enable efficient application of feature relevance indices to selection of feature subsets. A number of such methods have been applied to artificial and real-worl...