The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures

Haury, Anne-Claire; Gestraud, Pierre; Vert, Jean-Philippe

doi:10.1371/journal.pone.0028210

Cited by 329 publications

(244 citation statements)

References 33 publications

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“…In stability selection, a threshold value is used to determine the value of each feature. For each list, whether the examined feature exceeds the threshold value or not is determined and based on this judgement, a position is assigned to the feature [48]. The exponential weighting method extends the stability selection by assigning points based on e − r/s , where r denotes the rank of feature and s denotes the threshold value.…”

Section: Methodsmentioning

confidence: 99%

A feature selection model based on genetic rank aggregation for text sentiment classification

Onan

Korukoğlu

2016

Journal of Information Science

350

167

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Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification.

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Section: Methodsmentioning

confidence: 99%

A feature selection model based on genetic rank aggregation for text sentiment classification

Onan

Korukoğlu

2016

Journal of Information Science

350

167

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“…Novakovic et al compared 6 feature selection techniques on 2 data sets and they used classification accuracy as a criterion [21]. Haury et al compared 8 feature selection techniques on 4 data sets [22]. Silva et al compared 4 existing feature selection techniques (information gain, gain ratio, chi square, correlation) on 1 data set from the domain of agriculture [23].…”

Section: Feature Selectionmentioning

confidence: 99%

Handling Sparse Data Sets by Applying Contrast Set Mining in Feature Selection

Oreški¹,

Konecki²

2016

JSW

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A data set is sparse if the number of samples in a data set is not sufficient to model the data accurately. Recent research emphasized interest in applying data mining and feature selection techniques to real world problems, many of which are characterized as sparse data sets. The purpose of this research is to define new techniques for feature selection in order to improve classification accuracy and reduce the time required for feature selection on sparse data sets. The extensive comparison with benchmarking feature selection techniques on 64 sparse data sets was conducted. Results have shown superiority of contrast set mining techniques in more than 80% of the analysis on sparse data sets. This paper provides a study on the new methodologies and detected superiority in handling data sparsity.

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“…They compared the stability of three wrapper approaches. Haury et al [4] evaluated a number of feature ranking methods and one wrapper-based subset evaluation technique and considered stability in terms of how many features are in common between two subsets generated from independent subsamples of the original data. Dunne et al [5] considered wrappers using a 3-nearest neighbor learner and three choices of search technique, evaluating stability by resampling the original dataset and finding the Hamming distance between the various feature subset masks.…”

Section: Related Workmentioning

confidence: 99%

“…Another work, Haury et al [4], considers the role of overlap when considering the stability of gene subsets. In addition to other analysis of their datasets, the researchers consider the fraction of instances in common when comparing feature lists generated from subsamples of the original data which either have 80% or 0% overlap.…”

Section: Related Workmentioning

confidence: 99%

Stability of Three Forms of Feature Selection Methods on Software Engineering Data

Wang

Khoshgoftaar

Napolitano

2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-One of the major challenges when working with software metrics datasets is that some metrics may be redundant or irrelevant to software defect prediction. This may be addressed using feature (metric) selection, which chooses an appropriate subset of features for use in downstream computation. There are three major forms of feature selection: filter-based feature rankers, which uses statistical measures to assign a score to each feature and present the user with a ranked list; filter-based subset evaluation, which uses statistical measures on feature subsets to find the best choice; and wrapper-based subset selection, which builds classification models using different subsets to find the one which maximizes performance. Software practitioners are interested in which feature selection methods are best at providing the most stable feature subset in the face of changes to the data (here, the addition or removal of instances). In this study we select feature subsets using fifteen feature selection methods and then use our newly proposed Average Pairwise Tanimoto Index (APTI) to evaluate the stability of feature selection methods. We evaluate the stability of feature selection methods on a pair of subsamples generated by fixed-overlap partitions algorithm. Four different levels of overlap are considered in this study. Four software metric datasets from a real-world software project are used in this study. Results demonstrate that ReliefF (RF) is the most stable feature selection method and wrapper based feature subset selection shows least stability. In addition, as the overlap of partitions increased, the stability of the feature selection strategies increased.

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The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures

Cited by 329 publications

References 33 publications

A feature selection model based on genetic rank aggregation for text sentiment classification

A feature selection model based on genetic rank aggregation for text sentiment classification

Handling Sparse Data Sets by Applying Contrast Set Mining in Feature Selection

Stability of Three Forms of Feature Selection Methods on Software Engineering Data

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