Weighted Heuristic Ensemble of Filters

Feature selection has become increasingly important in data mining in recent years due to the rapid increase in the dimensionality of big data. However, the reliability and consistency of feature selection methods (filters) vary considerably on different data and no single filter performs consistently well under various conditions. Therefore, feature selection ensemble has been investigated recently to provide more reliable and effective results than any individual one but all the existing feature selection ensemble treat the feature selection methods equally regardless of their performance. In this paper, we present a novel framework which applies weighted feature selection ensemble through proposing a systemic way of adding different weights to the feature selection methods-filters. Also, we investigate how to determine the appropriate weight for each filter in an ensemble. Experiments based on ten benchmark datasets show that theoretically and intuitively adding more weight to ‘good filters’ should lead to better results but in reality it is very uncertain. This assumption was found to be correct for some examples in our experiment. However, for other situations, filters which had been assumed to perform well showed bad performance leading to even worse results. Therefore adding weight to filters might not achieve much in accuracy terms, in addition to increasing complexity, time consumption and clearly decreasing the stability

Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures

This paper introduces the use of an ant colony optimization (ACO) algorithm, called Ant System, as a search method in two wellknown feature subset selection methods based on correlation or consistency measures such as CFS (Correlation-based Feature Selection) and CNS (Consistency-based Feature Selection). ACO guides the search using a heuristic evaluator. Empirical results on twelve real-world classification problems are reported. Statistical tests have revealed that InfoGain is a very suitable heuristic for CFS or CNS feature subset selection methods with ACO acting as search method. The use of InfoGain is shown to be the significantly better heuristic over a range of classifiers. The results achieved by means of ACO-based feature subset selection with the suitable heuristic evaluator are better for most of the problems comparing with those obtained with CFS or CNS combined with Best First search.MICYT TIN2007-68084- C02-02MICYT TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752

Data Cleansing Meets Feature Selection: A Supervised Machine Learning Approach

This paper presents a novel procedure to apply in a sequential way two data preparation techniques from a different nature such as data cleansing and feature selection. For the former we have experienced with a partial removal of outliers via inter-quartile range whereas for the latter we have chosen relevant attributes with two widespread feature subset selectors like CFS (Correlation-based Feature Selection) and CNS (Consistency-based Feature Selection), which are founded on correlation and consistency measures, respectively. Empirical results on seven difficult binary and multi-class data sets, that is, with a test error rate of at least a 10%, according to accuracy, with C4.5 or 1-nearest neighbour classifiers without any kind of prior data pre-processing are outlined. Non-parametric statistical tests assert that the meeting of the aforementioned two data preparation strategies using a correlation measure for feature selection with C4.5 algorithm is significant better, measured with roc measure, than the single application of the data cleansing approach. Last but not least, a weak and not very powerful learner like PART achieved promising results with the new proposal based on a consistency measure and is able to compete with the best configuration of C4.5. To sum up, bearing in mind the new approach, for roc measure PART classifier with a consistency metric behaves slightly better than C4.5 and a correlation measureMICYT TIN2007-68084-C02- 02MICYT TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752

A Framework for Consistency Based Feature Selection

Feature selection is an effective technique in reducing the dimensionality of features in many applications where datasets involve hundreds or thousands of features. The objective of feature selection is to find an optimal subset of relevant features such that the feature size is reduced and understandability of a learning process is improved without significantly decreasing the overall accuracy and applicability. This thesis focuses on the consistency measure where a feature subset is consistent if there exists a set of instances of length more than two with the same feature values and the same class labels. This thesis introduces a new consistency-based algorithm, Automatic Hybrid Search (AHS) and reviews several existing feature selection algorithms (ES, PS and HS) which are based on the consistency rate. After that, we conclude this work by conducting an empirical study to a comparative analysis of different search algorithms

Feature Selection Approaches In Antibody Display

Molecular diagnostics tools provide specific data that have high dimensionality due to many factors analyzed in one experiment and few records due to high costs of the experiments. This study addresses the problem of dimensionality in melanoma patient antibody display data by applying data mining feature selection techniques. The article describes feature selection ranking and subset selection approaches and analyzes the performance of various methods evaluating selected feature subsets using classification algorithms C4.5, Random Forest, SVM and Naïve Bayes, which have to differentiate between cancer patient data and healthy donor data. The feature selection methods include correlation-based, consistency based and wrapper subset selection algorithms as well as statistical, information evaluation, prediction potential of rules and SVM feature selection evaluation of single features for ranking purposes

Bayesian variable selection with shrinking and diffusing priors

We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the well-known spike and slab Gaussian priors with a distinct feature, that is, the prior variances depend on the sample size through which appropriate shrinkage can be achieved. We show the strong selection consistency of the proposed method in the sense that the posterior probability of the true model converges to one even when the number of covariates grows nearly exponentially with the sample size. This is arguably the strongest selection consistency result that has been available in the Bayesian variable selection literature; yet the proposed method can be carried out through posterior sampling with a simple Gibbs sampler. Furthermore, we argue that the proposed method is asymptotically similar to model selection with the $L_0$ penalty. We also demonstrate through empirical work the fine performance of the proposed approach relative to some state of the art alternatives.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1207 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org