PC and TPDA algorithms are robust and well known prototype algorithms, incorporating constraint-based approaches for causal discovery. However, both algorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are ...

MOTIVATION Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature sp...

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a ‘consensus’ of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features o...

Now days, diagnosis of health conditions is a very critical and challenging task in field of medical science. Medical history data comprises of a number of tests essential to diagnose a particular disease and the diagnoses are based on the physician experience. The thyroid gland faced by physician which is one of the important organs in the body and also increases cellular activity. Data mining...

Datasets are becoming larger and there is an acute need to use data mining techniques to exploit the available data. The increasing size of the datasets poses a challenge to the data miners, which can be solved using two approaches – high speed computing systems, and pre-processing techniques. In this paper, we propose a solution combining the above two techniques using a distributed feature se...

Selecting a small subset of descriptors from a large pool to build a predictive quantitative structure-activity relationship (QSAR) model is an important step in the QSAR modeling process. In general, subset selection is very hard to solve, even approximately, with guaranteed performance bounds. Traditional approaches employ deterministic or stochastic methods to obtain a descriptor subset that...

Constrained Laplacian Score (CLS) is a recently proposed method for semi-supervised feature selection. It presented an outperforming performance comparing to other methods in the state of the art. This is because CLS exploits both unsupervised and supervised parts of data for selecting the most relevant features. However, the choice of the little supervision information (represented by pairwise...

A popular method for creating an accurate classifier from a set of training

Abstract— Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper ph...

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the p...