Multiclass Microarray Data Classification Using GA/ANN Method

Lin, Tsun-Chen; Liu, Ru-Sheng; Chao, Ya-Ting; Chen, Shuyuan

doi:10.1007/978-3-540-36668-3_129

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…For instance, Cho et al [4] used the ANN classification results as the GA fitness function in the cDNA microarray prediction. Bevilacqua et al [2] and Lin et al [9] applied the error rates returned by ANNs to determine the fitness of GAs in the cancer classification.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Tong

Mintram

2010

Int. J. Mach. Learn. & Cyber.

129

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Hybrid genetic algorithms (GA) and artificial neural networks (ANN) are not new in the machine learning culture. Such hybrid systems have been shown to be very successful in classification and prediction problems. However, little attention has been focused on this architecture as a feature selection method and the consequent significance of the ANN activation function and the number of GA evaluations on the feature selection performance. The activation function is one of the core components of the ANN architecture and influences the learning and generalization capability of the network. Meanwhile the GA searches for an optimal ANN classifier given a set of chromosomes selected from those available. The objective of the GA is to combine the search for optimum chromosome choices with that of finding an optimum classifier for each choice. The process operates as a form of co-evolution with the eventual objective of finding an optimum chromosome selection rather than an optimum classifier. The selection of an optimum chromosome set is referred to in this paper as feature selection. Quantitative comparisons of four of the most commonly used ANN activation functions against ten GA evaluation step counts and three population sizes are presented. These studies employ four data sets with high dimension and low significant datum instances. That is to say that each datum has a high attribute count and the unusual or abnormal data are sparse within the data set. Results suggest that the hyperbolic tangent (tanh) activation function outperforms other common activation functions by extracting a smaller, but more significant feature set. Furthermore, it was found that fitness evaluation sizes ranging from 20,000 to 40,000 within populations ranging from 200 to 300, deliver optimum feature selection capability. Again, optimum in this sense meaning a smaller but more significant feature set.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Tong

Mintram

2010

Int. J. Mach. Learn. & Cyber.

129

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“…Metode yang diusulkan pada penelitian ini mampu mengurangi dimensi fitur dengan mengidentifikasi subset gen yang paling informatif serta memperbaiki akurasi klasifikasi. Tsun-Chen Lin, Ru-Sheng Liu, Ya-Ting Chao, and Shu-Yuan Chen [20] tujuan dari penelitian yang dilakukan adalah untuk mengeksplorasi penggunaan data ekspresi gen dalam membedakan jenis kanker heterogen. Mereka menggunakan hybrid learning methodology algoritma genetika (GA) dan jaringan syaraf tiruan (JST) untuk menemukan subkumpulan gen yang optimal untuk klasi_kasi jaringan / kanker.…”

Section: Pendahuluanunclassified

Untitled

PAKAN

2019

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Klasifikasi kanker merupakan suatu topik penting dalam dunia kedokteran. Data mikro array merupakan suatu kumpulan data lengkap dengan ekspresi gen yang diambil dari beberapa pasien kanker. Penelitian ini bertujuan untuk melakukan klasifikasi kanker menggunakan ekspresi gen sebagai fitur yang dilatihkan pada jaringan syaraf tiruan. Penelitian ini menggunakan Elman Recurrent Neural Network sebagai mesin klasifikasi

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“…Also, very few overlaps exist between the genes selected from different sets of genes, viz., 14,28,42,56,70,84, and 98 gene sets. All these sets of genes perform well on the testing set (46 samples).…”

Section: Biological and Functional Analysismentioning

confidence: 99%

“…Recursive feature elimination with one-versus-all Support Vector Machine (SVM) was used in [1], [9], [10], [11], for cancer classification, using the Global Cancer Map (GCM) data set [1]. Many approaches have been implemented by integrating genetic algorithm with SVM or neural networks or fuzzy neural network for gene selection and classification [4], [12], [13], [14], [15], [19]. A comprehensive comparison of all the popular classification methods for different data sets is given by Statnikov et al [16].…”

Section: Introductionmentioning

confidence: 99%

ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented

Saraswathi

Sundaram

Sundararajan

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

103

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A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.

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Multiclass Microarray Data Classification Using GA/ANN Method

Cited by 3 publications

References 9 publications

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Untitled

ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented

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