A new feature selection method on classification of medical datasets: Kernel F-score feature selection

Polat, Kemal; Güneş, Salih

doi:10.1016/j.eswa.2009.01.041

Cited by 155 publications

(81 citation statements)

References 18 publications

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“…Chen demonstrated for the first time that feature selection strategies for SVM classification should be included [25]. Polat et al classified medical datasets using a hybrid system of feature selection and several classifiers and obtained better performance compared with the methods that did not utilize feature selection [26]. Akay proposed a breast cancer diagnosis method which integrated SVM and F-score feature selection [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

et al. 2013

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A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

et al. 2013

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“…Wisconsin Breast Cancer data set is not such popular, but our results can be compared to approaches (92-99%) presented and referenced in [10] for different rule set sizes. Although the efficiency results for the Heart Disease and Appendicitis data sets are not enough satisfactory, they are better or competitive to approaches presented and referenced in [19,25].…”

Section: Discussionmentioning

confidence: 96%

Membership Functions for Fuzzy Focal Elements

Porębski¹,

Straszecka²

2016

Archives of Control Sciences

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The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

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“…Kernel F-score feature selection [9] is a thresholdcomparison feature selection method. Its procedure is as follows: first an F-score value is defined in the kernel space.…”

Section: Kernel F-scorementioning

confidence: 99%

Image fusion for following-up brain tumor evolution

Ruan

Zhang

Liao

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination per four months): feature selection, SVMbased segmentation, and contour refinement. The training of the tumor is carried out only on the first MRI examination (before the treatment). Six feature selection methods are tested in our system. The quantitative comparisons of the methods' and the expert's manual traces demonstrate the effectiveness of the fusion by the feature selection. Results of following-up three patients over one year show that our system can provide a good tool to evaluate the therapeutic treatment.

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A new feature selection method on classification of medical datasets: Kernel F-score feature selection

Cited by 155 publications

References 18 publications

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

Membership Functions for Fuzzy Focal Elements

Image fusion for following-up brain tumor evolution

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