Classification Techniques of Neural Networks Using Improved Genetic Algorithms

Chen, Ming; Yao, Zhengwei

doi:10.1109/wgec.2008.23

Cited by 29 publications

(12 citation statements)

References 7 publications

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“…ANN is an adaptive system that changes its structure during the learning stage. Focusing too much on training data is likely the main problem in neural network training process [12]. Proper selection of number of neurons in the hidden layer and number of layers prevent excessive concentration on training data and thereby lead to fix this problem.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Customer Credit Risk Assessment using Artificial Neural Networks

Mohammadi¹,

Zangeneh²

2016

IJITCS

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Abstract-Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments. In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant. Regarding good performance and their ability of classification, generalization and learning patterns, Multilayer Perceptron Neural Network model trained using various Back-Propagation (BP) algorithms considered in designing an evaluation model in this study. The BP algorithms, Levenberg-Marquardt (LM), Gradient descent, Conjugate gradient, Resilient, BFGS Quasinewton, and One-step secant were utilized. Each of these six networks runs and trains for different numbers of neurons within their hidden layer. Mean squared error (MSE) is used as a criterion to specify optimum number of neurons in the hidden layer. The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms. At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment. In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.

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Section: A Artificial Neural Networkmentioning

confidence: 99%

Customer Credit Risk Assessment using Artificial Neural Networks

Mohammadi¹,

Zangeneh²

2016

IJITCS

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“…In [19] Mingrui Zhang et al, (2009) evaluated several validity measures in fuzzy clustering and developed a new measure for a fuzzy c-means algorithm which uses a Pearson correlation in its distance metrics. In [18] this paper Ming Chen et al, (2008) focused on a method of optimizing classifiers of neural network by Genetic Algorithm based on the principle of gene reconfiguration, and implemented classification by training the weight.…”

Section: Review Of Literaturementioning

confidence: 99%

Classification of Microarray Gene Expression Data by Gene Combinations using Fuzzy Logic MGC-FL

Bhuvaneswari¹

2012

IJCSEA

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Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.

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“…Various algorithms are developed for neural network learning including back propagation [5], [6], [7], [8] and genetic algorithms [9]. In this paper, we are proposing a new algorithm that improves the genetic algorithm.…”

Section: Neural Networkmentioning

confidence: 99%

Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

Kaur¹,

Nelapati²

2010

IJCIS

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The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. The Iris data problem is used as a bench-mark to compare the selective cloning technique with the conventional GA and the back-propagation algorithm. For comparative analysis, same neural network architecture is used for both the back propagation and the genetic algorithms. The selective cloning approach is based on the schema theorem. By using selective cloning, it has been shown that GA is 27.78% more efficient than the conventional GA and 83.33% more efficient than the back propagation approach. The results of selective cloning on other data sets are also discussed.

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Classification Techniques of Neural Networks Using Improved Genetic Algorithms

Cited by 29 publications

References 7 publications

Customer Credit Risk Assessment using Artificial Neural Networks

Customer Credit Risk Assessment using Artificial Neural Networks

Classification of Microarray Gene Expression Data by Gene Combinations using Fuzzy Logic MGC-FL

Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

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