Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy
Accuracy of a classifier or predictor is normally estimated with the help of confusion matrix, which is a useful tool for analyzing how well the classifier can recognize tuples of different classes. Calculation of classification accuracy of a predictor using confusion matrix for two classed attribute is simple. In case of multi classed attribute, we have to take accuracy of all the classes into consideration, to aggregate them to come with the actual accuracy of the particular classifier or predictor for that particular attribute. Here formulating this, weighted average classification accuracy has been introduced for the overall recognition rate of the classifier, which reflects how well the classifier recognizes tuples of various classes. Classification accuracy is being calculated for the classifiers BayesNet(BN), NaiveBayes(NB), J48 and Decision Table(DT) through weighted average accuracy formulation and the trend of the accuracy values for different number of instances is displayed in tables, which shows the flawless calculation.
Jiawei Han and Micheline Kamber, Book on” Data Mining: Concepts and Techniques”, 2nd ed., Morgan Kaufmann Publishers, March 2006. ISBN 978-1-55860-901-3.
Vikas Mittal, D. Singh and L.M. Saini; “Land Cover Classification using EM Algorithm based Multi-Polarized ALOS PALSAR Image Fusion”; IEEE, 2013, Page(s) 5pp.
Weiqi Zhou; “An Object-Based Approach for Urban Land Cover Classification Integrating LiDAR Height and Intensity Data”; IEEE, 2013, pp.928-931.
Sathish kumar Samiappan, Saurabh Prasad and Lori M Bruce; “Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Based Ensemble Classification for Hyperspectral Image Analysis”; IEEE, 2013, pp. 792-800.
S. Rajesh and S. Arivazhagan ; “Land CoverLand Use Mapping using Different Wavelet Packet Transforms for LISS IV Imagery”; IEEE, 2011, pp. 103-108.
M. Seetha, K. V. N. Sunitha, D.V. Lalitha Parameswari, G. Ravi; “Accuracy Assessment of Object Oriented and Knowledge Base Image Classification using P-Trees”; IEEE, 2010, pp. 760-763.
Catherine Champagne, Heather McNairn, Bahram Daneshfar, Jiali Shang; “A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada”; Elsevier, 2014, pp. 44-52.
Karolina D. Fieber, Ian J. Davenport, James M. Ferryman, Robert J. Gurney, Jeffrey P. Walker, Jorg M. Hacker, “Analysis of full-waveform LiDAR data for classification of an orange orchard scene”; Elsevier, 2013, pp. 63-82.
Mariana Belgiu, Lucian Draˇgut, Josef Strobl; “Quantitative evaluation of variations in rule-based classifications of land cover in urban neighborhoods using WorldView-2 imagery”; Elsevier, 2013, pp. 205-215.
http://en.wikipedia.org/wiki/Confusion_matrix last accessed on 10/04/13.
http://www.dicom.uninsubria.it/~marco.vanetti/cfmatrix last accessed on 15/05/13.
Wenkai Li and Qinghua Guo, “A New Accuracy Assessment Method for One-Class Remote Sensing Classification”, IEEE, 2013, pp. 1-12.
P.K.A. Chitra and S. Appavu Alias Balamurugan, “Benchmark Evaluation of classification methods for single label learning with R”, IEEE, 2013, pp. 746-752.
Asuncion A. and Newman D.J. (2007) UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science. [Online] Available from: http://www.ics.uci.edu/~mlearn/MLRepository.html last accessed on 11/02/13.
Jensen F.V. “Introduction to Bayesian Networks”. Denmark: Hugin Expert A/S, 1993.
Wang Z. and I. Webb G., “Comparison of lazy bayesian rule and tree-augmented bayesian learning”, IEEE, 2002, pp. 490 – 497.
Shi Z., Huang Y. and Zhang S., “Fisher score based naive Bayesian classifier”, IEEE, 2005, pp. 1616-1621.
Xie Z. and Zhang Q., “A study of selective neighborhood-based naïve bayes for efficient lazy learning”. 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004.
Santafe G., Loranzo J.A. and Larranaga P., “Bayesian model averaging of naive bayes for clustering”, IEEE, 2006, Page(s) 1149 – 1161.
http://en.wikipedia.org/wiki/C4.5_algorithm last accessed on 21/03/13.
Alicia Y.C. Tang, Nur Hanani Azami and Norfaezah Osman, “Application of Data Mining Techniques in Customer relationship Management for An Automobile Company”, IEEE, 2011, Page(s) 7pp.