Abstract
In this paper, Arif Index is proposed that can be used to assess the discrimination power of features in pattern classification problems. Discrimination power of features play an important role in the classification accuracy of a particular classifier applied to the pattern classification problem. Optimizing the performance of a classifier requires a prior knowledge of maximum achievable accuracy in pattern classification using a particular set of features. Moreover, it is also desirable to know that this set of features is separable by a decision boundary of any arbitrary complexity or not. Proposed index varies linearly with the overlap of features of different classes in the feature space and hence can be used in predicting the classification accuracy of the features that can be achieved by some optimal classifier. Using synthetic data, it is shown that the predicted accuracy and Arif index are very strongly correlated with each other (R 2 = 0.99). Implementation of the index is simple and time efficient. Index was tested on Arrhythmia beat classification problem and predicted accuracy was found to be in consistent with the reported results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17(2/3), 107–145 (2001)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Cluster validity methods: Part 1. In: SIGMOD Record, vol. 31(2), pp. 40–45 (2002)
Dunn, J.C.: Well Separated Clusters and Optimal Fuzzy Partitions. J. Cybern. 4, 95–104 (1974)
Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)
Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(4), 841–846 (1991)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846–850 (1971)
Fowlkes, E., Mallows, C.: A method for comparing two hierarchical clustering. Journal of the American Association 78 (1983)
Mirkin, B.G., Cherny, L.B.: On a distance measure between partitions of a finite set. Automation and remote Control 31(5), 91–98 (1970)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification, 193–218 (1985)
Xu, R., Wunsch, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Dash, M., Liu, H., Xu, X.: 1+1>2: Merging Distance and Density Based Clustering. In: Proceedings of Seventh International Conference on Database Systems for Advanced Applications, pp. 32–39 (2001)
Xu Ester, X., Kriegel, M., Sander, H.-P.: A distribution-based clustering algorithm for mining in large spatial databases. In: Proceedings of 14th International Conference on Data Engineering, pp. 324–331 (1998)
Hinneburg, A., Keim, D.A.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 58–65 (1998)
Afsar, F.A., Arif, M.: Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiological Measurement 29, 555–570 (2008)
Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical Engineering 46(2), 179–185 (1999)
Prasad, G.K., Sahambi, J.S.: Classification of ECG arrhythmias using multiresolution analysis and Neural Networks. In: Conference on Convergent Technologies, India (2003)
Yu, S.N., Chen, Y.H.: Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters 28(10), 1142–1150 (2007)
Mark, R., Moody, G.: MIT-BIH Arrhythmia Database Directory. MIT Press, Cambridge (1988)
Usman Akram, M.: Application of Prototype Based Fuzzy Classifiers for ECG based Cardiac Arrhythmia Recognition, BS Thesis, Pakistan Institute of Engineering and Applied Sciences (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arif, M., Afsar, F.A., Akram, M.U., Fida, A. (2009). Arif Index for Predicting the Classification Accuracy of Features and Its Application in Heart Beat Classification Problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-01307-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01306-5
Online ISBN: 978-3-642-01307-2
eBook Packages: Computer ScienceComputer Science (R0)