A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

Chiang, Jung-Hsien; Ho, Shing-Hua

doi:10.1109/tnb.2008.2000142

Cited by 46 publications

(4 citation statements)

References 36 publications

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“…In [ 18 ], Jung-Hsien Chiang and Shing-Hua Ho have introduced a prediction approach that uses a radial basis function NN and a rough-based method of feature selection. The method can be used to discover the unique features and defining centers close to the right ones.…”

Section: Related Workmentioning

confidence: 99%

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Gene Expression-Assisted Cancer Prediction Techniques

Thakur

Batra

Luthra

et al. 2021

Journal of Healthcare Engineering

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Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages.

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Section: Related Workmentioning

confidence: 99%

“…ere are some problems related to noise and dimensionality. In [18], Jung-Hsien Chiang and Shing-Hua Ho have introduced a prediction approach that uses a radial basis function NN and a rough-based method of feature selection.…”

Section: Related Workmentioning

confidence: 99%

Gene Expression-Assisted Cancer Prediction Techniques

Thakur

Batra

Luthra

et al. 2021

Journal of Healthcare Engineering

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“…This metric is one of the main indicators for evaluating classification performance by measuring the errors (misclassifications) incurred by a classifier (Chiang and Ho 2008;De Paz et al 2013;Mohapatra and Chakravarty 2015;Alsalem et al 2018). The error rate is expressed as shown in Equation ( 35).…”

Section: Error Ratementioning

confidence: 99%

Hybrid intelligent predictive maintenance model for multiclass fault classification

et al. 2023

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Data recorded from monitoring the health condition of industrial equipment are often high-dimensional, nonlinear, nonstationary and characterised by high levels of uncertainty. These factors limit the efficiency of machine learning techniques to produce desirable results when developing effective fault classification frameworks. This paper sought to propose a hybrid artificial intelligent predictive maintenance model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) optimised by the combination of Coupled Simulated Annealing and Nelder-Mead Simplex optimisation algorithms (ICEEMDAN-PCA-LSSVM). Here, ICEEMDAN was first employed as a denoising technique to decompose signals into series of Intrinsic Mode Functions (IMFs) of which only relevant IMFs containing the relevant fault features were retained for signal reconstruction. PCA was then employed as a dimension reduction technique through which the resulting set of uncorrelated features extracted served as input for LSSVM for classifying various fault types. The proposed technique is compared with three established methods (Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Artificial Neural Network (ANN)) with multiclass classification capabilities. The various techniques were tested on an experimental UCI machine learning benchmark data obtained from multi-sensors of a hydraulic test rig. The results from the analysis revealed that the proposed ICEEMDAN-PCA-LSSVM technique is versatile and outperformed all the compared classifiers in terms of accuracy, error rate and other evaluation metrics considered. The proposed hybrid technique drastically reduced the redundancies and the dimension of features, allowing for the efficient consideration of relevant features for the enhancement of classification accuracy and convergence speed.

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“…A radial basis function network (RBFN) was used to classify the gene sequences. A novel rough set-based feature selection technique was presented in [ 27 ]. The benefit of adopting this method was to avoid gene expression clustering.…”

Section: Related Literaturementioning

confidence: 99%

Convergent learning–based model for leukemia classification from gene expression

Mallick

Mohapatra

Chae

et al. 2020

Pers Ubiquit Comput

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Microarray data analysis is a major challenging field of research in recent days. Machine learning–based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning techniques to mitigate these types of problems. Due the presence of more number of hidden layers, it can easily handle the big amount of data. We have presented a method of classification to understand the convergence of training deep neural network (DNN). The assumptions are taken as the inputs do not degenerate and the network is over-parameterized. Also the number of hidden neurons is sufficiently large. Authors in this piece of work have used DNN for classifying the gene expressions data. The dataset used in the work contains the bone marrow expressions of 72 leukemia patients. A five-layer DNN classifier is designed for classifying acute lymphocyte (ALL) and acute myelocytic (AML) samples. The network is trained with 80% data and rest 20% data is considered for validation purpose. Proposed DNN classifier is providing a satisfactory result as compared to other classifiers. Two types of leukemia are classified with 98.2% accuracy, 96.59% sensitivity, and 97.9% specificity. The different types of computer-aided analyses of genes can be helpful to genetic and virology researchers as well in future generation.

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A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

Cited by 46 publications

References 36 publications

Gene Expression-Assisted Cancer Prediction Techniques

Gene Expression-Assisted Cancer Prediction Techniques

Hybrid intelligent predictive maintenance model for multiclass fault classification

Convergent learning–based model for leukemia classification from gene expression

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