A New Performance Evaluation Metric for Classifiers: Polygon Area Metric

Author:

Aydemir Onder

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Psychology (miscellaneous),Mathematics (miscellaneous)

Reference11 articles.

1. Aydemir, O., & Kayikcioglu, T. (2011). Wavelet transform based classification of invasive brain computer interface data. Radioengineering, 20(1), 31–38.

2. Aydemir, O., & Kayikcioglu, T. (2013). Comparing common machine learning classifiers in low-dimensional feature vectors for brain computer interface applications. International Journal of Innovative Computing Information and Control, 9(3), 1145–1157.

3. Chu, C., Ni, Y., Tan, G., Saunders, C. J., & Ashburner, J. (2011). Kernel regression for fMRI pattern prediction. NeuroImage, 56(2), 662–673.

4. Dixon, S. J., & Brereton, R. G. (2009). Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometrics and Intelligent Laboratory Systems, 95(1), 1–17.

5. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

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