Book ChapterDOI
Forest Type Classification: A Hybrid NN-GA Model Based Approach
Sankhadeep Chatterjee,Subhodeep Ghosh,Subham Dawn,Sirshendu Hore,Nilanjan Dey +4 more
- pp 227-236
TLDR
The authors have proposed a GA trained Neural Network classifier to tackle the task of classify tree species and one mixed forest class using geographically weighted variables calculated for Cryptomeria japonica and Chamaecyparis obtusa.Abstract:
Recent researches have used geographically weighted variables calculated for two tree species, Cryptomeria japonica (Sugi, or Japanese Cedar) and Chamaecyparis obtusa (Hinoki, or Japanese Cypress) to classify the two species and one mixed forest class. In machine learning context it has been found to be difficult to predict that a pixel belongs to a specific class in a heterogeneous landscape image, especially in forest images, as ground features of nearly located pixel of different classes have very similar spectral characteristics. In the present work the authors have proposed a GA trained Neural Network classifier to tackle the task. The local search based traditional weight optimization algorithms may get trapped in local optima and may be poor in training the network. NN trained with GA (NN-GA) overcomes the problem by gradually optimizing the input weight vector of the NN. The performance of NN-GA has been compared with NN, SVM and Random Forest classifiers in terms of performance measures like accuracy, precision, recall, F-Measure and Kappa Statistic. The results have been found to be satisfactory and a reasonable improvement has been made over the existing performances in the literature by using NN-GA.read more
Citations
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Proceedings ArticleDOI
Cuckoo search coupled artificial neural network in detection of chronic kidney disease
TL;DR: The experimental results suggest that NN-CS based model is capable of detecting CKD more efficiently than any other existing model.
Journal ArticleDOI
Application of cuckoo search in water quality prediction using artificial neural network
Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Soumya Sen,Aboul Ella Hassanien +5 more
TL;DR: The proposed cuckoo search (CS) gradually minimises an objective function; namely the root mean square error (RMSE) in order to find the optimal weight vector for the artificial neural network (ANN).
Proceedings ArticleDOI
Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach
Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Soumya Sen,Takaaki Goto,Narayan C. Debnath +5 more
TL;DR: The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN) to improve its performance over its traditional counterparts.
Book ChapterDOI
Electrical Energy Output Prediction Using Cuckoo Search Based Artificial Neural Network
TL;DR: The results established the improved performance of the CS based NN compared to the multilayer perceptron feed-forward neural network and the NN-PSO (particle swarm optimization) in terms of root mean squared error.
Proceedings ArticleDOI
Hybrid modified Cuckoo Search-Neural Network in chronic kidney disease classification
Sankhadeep Chatterjee,Simona Dzitac,Soumya Sen,Noemi Clara Rohatinovici,Nilanjan Dey,Amira S. Ashour,Valentina Emilia Balas +6 more
TL;DR: The experimental results depicted that the NN-MCS has the ability to detect CKD more efficiently compared to any other existing model.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
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Chih-Chung Chang,Chih-Jen Lin +1 more
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Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.