Applying the Fisher score to identify Alzheimer's disease-related genes

Yang, Jung Eun; Yl, Liu; Cs, Feng; Gq, Zhu

doi:10.4238/gmr.15028798

Cited by 30 publications

(9 citation statements)

References 17 publications

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“…Mutual information–based filter methods use mutual information to evaluate the relevance of features to class labels and the redundancy of candidate features. However, they suffer from the problem that the objective function only uses a single statistic measure of a dataset (eg, standard deviation, information gain [42,47], or Fisher score [48]), while ignoring the fusion of multiple measures. For example, a standard deviation–based filter model relies on the distance between feature value and mean value for feature selection.…”

Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

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Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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“…To evaluate the performance of FSACE in terms of classification accuracy, FSACE algorithm is compared with several state-of-the-art feature selection algorithms, including EGGS ( Chen et al, 2017 ), EGGS-FS ( Yang et al, 2016 ), MEAR ( Xu et al, 2009 ), Fisher ( Saqlain et al, 2019 ), and Lasso ( Tibshirani, 1996 ). According to the change trend of Fisher scores of six gene datasets, we select the top-200 genes as the reduction set for Fisher algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification

Zhang

2021

Front. Genet.

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Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy.

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“…This part of the experiment compares the BONJE algorithm with four other advanced feature selection algorithms in the low-dimensional data set from the perspective of the number of selected features and the classification accuracy of KNN and SVM classifiers. The four advanced feature selection algorithms are: (1) Classic Rough Set Algorithm (RS) [1], (2) Neighborhood Rough Set Algorithm (NRS) [40], (3) Covering Decision Algorithm (CDA) [41], (4) Maximum Decision Neighborhood Rough Set Algorithm (MDNRS) [35]. show the experimental results of five different feature selection algorithms.…”

Section: The Performance Of Bonje Algorithm On Low-dimensional Data Setsmentioning

confidence: 99%

Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

Yuan

et al. 2021

Entropy

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Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.

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Applying the Fisher score to identify Alzheimer's disease-related genes

Cited by 30 publications

References 17 publications

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification

Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

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