Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization

Elyasigomari, Vahid; Mirjafari, Mehran; Screen, Hazel R. C.; Shaheed, M. H.

doi:10.1016/j.asoc.2015.06.015

Cited by 54 publications

(9 citation statements)

References 35 publications

(43 reference statements)

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“…Let N TP , N FP , N TN , and N FN , respectively, denote the number of test dataset that the classifier has determined as true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. A 2 × 2 confusion matrix TP FP TN FN can be formed from the above values (Fatourechi et al, 2008 ; Elyasigomari et al, 2015 ). In order to quantify classification performance, we used sensitivity, specificity , accuracy , precision, G-mean and F-Measure to report classification performance (Fatourechi et al, 2008 ; He and Garcia, 2009 ; Jamal et al, 2014 ; Mumtaz et al, 2017 ) 1 :…”

Section: Resultsmentioning

confidence: 99%

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Wang

2018

Front. Hum. Neurosci.

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A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. This calculation, which considers the differences between the sequence pairs to build a reasonable partition, can improve the classification performance. On this basis, however, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under circumstances of insufficient a priori knowledge of domain dependency, such as denoising, feature extraction on a special patient, etc. To address these problems by (1) effectively calculating the MI matrix (synchronization pattern) and (2) accurately classifying the seizure or non-seizure state, this study first accurately measures the synchronization between channel pairs in terms of affinity propagation clustering partition MI (APCPMI). The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs into a correlation matrix. Finally, a cross-layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify seizure or non-seizure states automatically. Experiments are performed using the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states are identified with an accuracy, sensitivity, and specificity of 0.9793 ± 0.002, 0.9942 ± 0.0005, and 0.9676 ± 0.003, respectively; the resulting performance is superior to those achieved by most existing methods over the same dataset. Furthermore, the approach alleviates the necessity for strictly preprocessing (denoising, removing interferences and artifacts) the EEG data using prior knowledge, which is usually required by existing approaches.

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Section: Resultsmentioning

confidence: 99%

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Wang

2018

Front. Hum. Neurosci.

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“…Individual classifiers, ensembles thereof, and hybrid systems have often been used to diagnose various diseases. Several techniques have been applied on medical data to improve such diagnosing efficacy, regarding performance parameters such as prediction accuracy, sensitivity, and specificity [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Nalluri

Kannan

Manisha

et al. 2017

Journal of Healthcare Engineering

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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“…In the past few years, many classification algorithms [2, 3, 5 –12 ] have been applied to tumour classification based on the gene expression data. For instance, rough set theory was used for cancer prediction [9 ], data clustering with optimisation was applied for cancer classification [10 ], and random forest was used for analysing acute leukaemia [12 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

Jin

2016

IET syst. biol.

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A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective.

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Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization

Cited by 54 publications

References 35 publications

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

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