M. Yousef et al. / J. Biomedical Science and Engineering 3 (2010) 247-252
Copyright © 2010 SciRes.
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JBiSE
Table 1. One-Class results.
MCC ACC TN TP Method
0.69 0.81 0.779 0.91 OC-SVM
0.89 0.89 0.86 OC-Gaussian 0.75
0.87 0.87 0.90 OC-Kmeans 0.77
0.77 0.77 0.77 OC-PCA 0.55
0.89 0.89 0.87 OC-Knn 0.76
Tbale 2. Two class results.
Method TP TN Acc MCC
Naïve Bayes 0.93 0.99 0.99 0.93
SVM 0.98 0.9974 99.3 0.977
KNN4 0.858 0.952 92.88 0.813
C4.5 0.912 0.978 96.23 0.89
Random Forest 0.958 0.993 98.44 0.951
than the two-class approaches. During the training stage
of the one-class classifier we have set the 10% of the
positive data, whose likelihood is furthest from the true
positive data based on the distribution, as “outliers” in
order to produce a compact classifier. This factor might
cause a loss of information about the target class which
might also result in reducing performance compared to
the two class approach.
6. CONCLUSIONS
The current results show that it is possible to build up a
classifier based only on positive examples yielding a
reasonable performance. Moreover, more efforts are re-
quired to figure out more biological features to be used
in the design of the one-class classifier to improve the
performance. However, we hypothesize that taken 10%
of the training data as “outlier” is the cause of reducing
the one-class performance.
7. ACKNOWLEDGEMENTS
This project is funded in part under a grant with the ESHKOL scholar-
ship program of the ministry of science culture and sport to support
KW.
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