Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach

Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

doi:10.1007/978-3-642-37189-9_5

Cited by 21 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, when and how to apply these operators, and the parameter settings in GAs are also key factors influencing their performance on feature selection. [19], [20], [40], [113], [114], [115], [116], [117], [15], [118], [119], [120], [121], [122], [123], [124], [125], [126] Filter [41], [127], [128] [129], [130] B. GP for Feature Selection Table II shows typical works on GP for feature selection. Compared with GAs and PSO, there are a much smaller number of works on GP for feature selection.…”

Section: A Gas For Feature Selectionmentioning

confidence: 99%

“…Later, to take different advantages of different measures, multiple filter measures were used to rank features and a set of features were selected according to each measure. The combination of these features was used as input to GP for further feature selection [116], [119]. However, a potential limitation is that individual feature ranking may remove potentially useful features without considering their interactions with other features.…”

Section: A Gas For Feature Selectionmentioning

confidence: 99%

“…Human action recognition [84], [201] (2) Gene analysis [49], [103], [15], [118], [119], [122], [139], [140], [143], [145], [171], [189] (2)…”

Section: Measures In Filter Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

Self Cite

1,471

635

View full text Add to dashboard Cite

Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

show abstract

Section: A Gas For Feature Selectionmentioning

confidence: 99%

Section: A Gas For Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue

Zhang

Browne

et al. 2016

IEEE Trans. Evol. Computat.

Self Cite

1,471

635

View full text Add to dashboard Cite

show abstract

“…In our previous research, we used the features selected by GP with different fitness functions and achieved a certain level of success on MS and LC-MS data sets (Ahmed et al, 2012(Ahmed et al, , 2013a. Moreover, in Ahmed et al (2013b), different feature selection metrics were used with GP to further select a smaller number of features. The features selected by GP were used and those features managed to improve the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

2014

Self Cite

View full text Add to dashboard Cite

Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain-(IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

show abstract

“…Much work has been devoted to classification using GP and ABC [18][19][20][21][22][23][24][25]. GP based feature selection age layered population structure as a new algorithm for feature selection with classification was compared with other GP versions in [18].…”

Section: Classificationmentioning

confidence: 99%

Feature Selection for Classification with Artificial Bee Colony Programming

Arslan¹,

Öztürk²

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

View full text Add to dashboard Cite

Feature selection and classification are the most applied machine learning processes. In the feature selection, it is aimed to find useful properties containing class information by eliminating noisy and unnecessary features in the data sets and facilitating the classifiers. Classification is used to distribute data among the various classes defined on the resulting feature set. In this chapter, artificial bee colony programming (ABCP) is proposed and applied to feature selection for classification problems on four different data sets. The best models are obtained by using the sensitivity fitness function defined according to the total number of classes in the data sets and are compared with the models obtained by genetic programming (GP). The results of the experiments show that the proposed technique is accurate and efficient when compared with GP in terms of critical features selection and classification accuracy on well-known benchmark problems.

show abstract

Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach

Cited by 21 publications

References 21 publications

A Survey on Evolutionary Computation Approaches to Feature Selection

A Survey on Evolutionary Computation Approaches to Feature Selection

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

Feature Selection for Classification with Artificial Bee Colony Programming

Contact Info

Product

Resources

About