Feature Extraction and Classification Technique in Neural Network
|
10.5120/4383-6070 |
Kaushik Adhikary and Amit Kumar. Article: Feature Extraction and Classification Technique in Neural Network. International Journal of Computer Applications 35(3):29-35, December 2011. Full text available. BibTeX
@article{key:article,
author = {Kaushik Adhikary and Amit Kumar},
title = {Article: Feature Extraction and Classification Technique in Neural Network},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {35},
number = {3},
pages = {29-35},
month = {December},
note = {Full text available}
}
Abstract
Feature extraction is the heart of an object recognition system. In recognition problem, features are utilized to classify one class of object from another. The original data is usually of high dimensionality. The objective of the feature extraction is to classify the object, and further to reduce the dimensionality of the measurement space to a space suitable for the application of object classification techniques. In the feature extraction process, only the salient features necessary for the recognition process are retained such that the classification can be implemented on a vastly reduced feature set. In paper we are going to discuss the feature as well as classification technique used in neural network.
References
- Peng, H.C., Long, F., and Ding, C., Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min- redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1226–1238, 2005.
- Nguyen, H., Franke, K., Petrovic, S. (2010) "Towards a Generic Feature-Selection Measure for Intrusion Detection", In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.
- M. Hall 1999, Correlation-based Feature Selection for Machine Learning
- Hai Nguyen, Katrin Franke, and Slobodan Petrovic, Optimizing a class of feature selection measures, Proceedings of the NIPS 2009 Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML), Vancouver, Canada, December 2009.
- Muhammed H. Hamid, “What is Feature¬Vector Based Analysis” Swedish Society for Automated Image Analysis Symposium, 2001, pp 14 – 15.
- Guyon Isabelle, Elisseeff André, “An Introduction to Variable and Feature Selection” 2003, pp 1157-1182.
- Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar, “Ranking a Random Feature for Variable and Feature Selection” 2003, pp 1399- 1414.
- Armand S, Blumenstein M, Muthukkumarasamy V, “Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural- based Classification” International Joint Conference on Neural Networks, IJCNN, Vol 06, 2006,pp 684 – 691.
- Blumenstein M, Liu X.Y, Verma B, “A modified direction feature for cursive character recognition” International Joint Conference on Neural Networks, IEEE Proceedings. 2004, Vol 4, pp 2983 – 2987
- Paola, J. D. and Schowengerdt, R. A., “Comparisons of neural networks to standard techniques for image classification and correlation,” Proceedings of the International Geo-science and Remote Sensing Symposium (IGARSS’94), 1404-1406, Pasadena, USA, 1994