Fuzzy system for classification of microarray data using a hybrid ant stem optimisation algorithm

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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A hybrid Artificial Neural Network-Genetic Algorithm approach for classification of microarray data

2015 23nd Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2015.7129828 ◽

2015 ◽

Cited By ~ 1

Author(s):

Mehmet Bilen ◽

Ali Hakan Isik ◽

Tuncay Yigit

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Microarray Data ◽

Genetic Algorithm Approach ◽

Artificial Neural ◽

Hybrid Artificial Neural Network

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An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

Journal of Artificial Evolution and Applications ◽

10.1155/2009/803973 ◽

2009 ◽

Vol 2009 ◽

pp. 1-10 ◽

Cited By ~ 11

Author(s):

Nicoletta Dessì ◽

Barbara Pes

Keyword(s):

Microarray Data ◽

Selection Criteria ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Research Area ◽

Svm Classifier ◽

Ranking Methods ◽

Wrapper Approach ◽

Relevant Gene

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.

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