X. Wu, J. Kris'ar, and P. Mahl'en. Noise Handling with Extension Matrices. International Journal of Artificial Intelligence Tools, 5(1996), 81--97.  Home/Search Document Details and Download
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RIAC: A Rule Induction Algorithm Based on Approximate.. - Hamilton, Shan, Cercone (1996) (3 citations) (Correct)
....RIAC has been evaluated by comparing its performance with other methods on some standard machine learning problems. To evaluate RIAC, we measured the prediction accuracy of the learned rules on test examples. 7. 1 Performance Comparison on the MONK s Problems The MONK s problems (Thrun et al. 1991; Wu et al. 1995) are three artificially constructed problems and are well studied. The three Monk s problems, call MONK 1 ; MONK 2 and MONK 3 , represent three different types of learning tasks with two binary and four nominal attributes. The example space contains 432 (3 Theta 3 Theta 2 Theta 3 Theta 4 ....
....Accuracy MONK TRN1 MONK TRN2 MONK TRN3 MONK TST1 MONK TST2 MONK TST3 C4.5 59 113 27 82.4 69.7 90.3 HCV 7 39 18 100.0 81.7 90.3 RIAC 8 37 23 100.0 91.90 95.83 Table 1: Performance Comparison on the MONK s Problems. A performance comparison of RIAC with C4.5 (Quinlan 1992) and HCV (Wu et al. 1995) on the MONK s problems is shown in Table 1. The results for C4.5 and HCV are from (Wu et al. 1995) # Rule is the number of rules generated by the learning algorithm and Accuracy is the classification accuracy when the generated rules set are used to classify the testing set. In all cases, ....
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Wu, X.D., Krisar, J., and Mahlen, P. 1995. "Noise Handling with Extension Matrices," Proc. of the 7th International Conference on Tools with Artificial Intelligence, Washington, D.C., Nov. 1995. pp. 190-197.
A Decision Support Tool for Tuning Parameters in a Machine .. - Postema, Menzies, Wu (1997) (Correct)
....Databases and Domain Theories. The inputs for the machine learners include the dictionary, training and testing files, which describe the attributes, and include training and testing examples respectively. 4. 1 Baseline The experimental results can be compared to those obtained by Wu et al. [14]. The best results in terms of predictive accuracy for each problem is given in bold. 4.2 Results Table 1: Success Analysis of Results Domain Success Analysis fi Psi Phi Wine 4 5 Bupa 4 2 Labor Neg 4 2 Swiss 5 3 1 Cleveland 2 1 Cleveland 5 1 Va 5 1 Va 2 1 Crx 1 ....
X. Wu, J. Kris'ar, and P. Mahl'en. Noise Handling with Extension Matrices. In International Journal of Artificial Intelligence Tools, 5(1996), 81-97.
A Tuning Aid For Discretization In Rule Induction - Postema, Wu, Menzies (1997) (Correct)
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X. Wu, J. Kris'ar, and P. Mahl'en. Noise Handling with Extension Matrices. International Journal of Artificial Intelligence Tools, 5(1996), 81--97.
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