A relative decision entropy-based feature selection approach

Jiang, Feng; Sui, Yuefei; Zhou, Lin

doi:10.1016/j.patcog.2015.01.023

Cited by 68 publications

(28 citation statements)

References 61 publications

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“…The other important characteristic which distinguishes the search methods is the approach which is used to evaluate the goodness of a feature/feature subset or criterion function, J(· ). The filter approach relies on a metric derived from statistical, information theory or rough sets to estimate J(· ) [11,24,25,26,27,28,29]. Since, J(· ) is estimated indirectly, the induction of the classifier is not required.…”

Section: Filter Vs Wrappermentioning

confidence: 99%

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

Hafiz

Swain

Patel

et al. 2018

Pattern Recognition

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This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.

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Section: Filter Vs Wrappermentioning

confidence: 99%

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

Hafiz

Swain

Patel

et al. 2018

Pattern Recognition

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“…Next, the selected subset is scored by a learner. In addition to the wrapper method, FeatureSelect includes 5 filter methods which can score features using Laplacian [40], entropy [41], Fisher [42], Pearson-correlation [43], and mutual information [44] scores. After scoring, it selects features based on their scores.…”

Section: (Iv) Online-basedmentioning

confidence: 99%

FeatureSelect: a software for feature selection based on machine learning approaches

2019

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Background: Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. Results: In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. Conclusions: FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements.

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“…In the context of the classification methods, the use of entropy-based metrics is typically restricted to the feature selection [40,41,42], the process where a subset of relevant features (variables, predictors) is selected and used for the definition of the classification model.…”

Section: Shannon Entropymentioning

confidence: 99%

An Entropy Based Algorithm for Credit Scoring

Saia¹,

Carta²

2016

Lecture Notes in Business Information Processing

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Part 6: Decision Support in EISInternational audienceThe request of effective credit scoring models is rising in these last decades, due to the increase of consumer lending. Their objective is to divide the loan applicants into two classes, reliable or unreliable, on the basis of the available information. The linear discriminant analysis is one of the most common techniques used to define these models, although this simple parametric statistical method does not overcome some problems, the most important of which is the imbalanced distribution of data by classes. It happens since the number of default cases is much smaller than that of non-default ones, a scenario that reduces the effectiveness of the machine learning approaches, e.g., neural networks and random forests. The in Maximum Entropy (DME) approach proposed in this paper leads toward two interesting results: on the one hand, it evaluates the new loan applications in terms of maximum entropy difference between their features and those of the non-default past cases, using for the model training only these last cases, overcoming the imbalanced learning issue; on the other hand, it operates proactively, overcoming the cold-start problem. Our model has been evaluated by using two real-world datasets with an imbalanced distribution of data, comparing its performance to that of the most performing state-of-the-art approach: random forests

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A relative decision entropy-based feature selection approach

Cited by 68 publications

References 61 publications

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

FeatureSelect: a software for feature selection based on machine learning approaches

An Entropy Based Algorithm for Credit Scoring

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