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waveform-5000

waveform-5000

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Waveform Database Generator (written in C) 2. Source: (a) Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984). Classification and Regression Trees. Wadsworth International Group: Belmont, California. (see pages 43-49). (b) Donor: David Aha (c) Date: 11/10/1988 3. Past Usage: 1. CART book (above): -- Optimal Bayes classification rate: 86% accuracy -- CART decision tree algorithm: 72% -- Nearest Neighbor Algorithm: 78% -- 300 training and 5000 test instances 4. Relevant Information: -- 3 classes of waves -- 21 attributes, all of which include noise -- See the book for details (49-55, 169) -- waveform.data.Z contains 5000 instances 5. Number of Instances: chosen by user 6. Number of Attributes: -- 21 attributes with continuous values between 0 and 6 7. Attribute Information: -- Each class is generated from a combination of 2 of 3 "base" waves -- Each instance is generated f added noise (mean 0, variance 1) in each attribute -- See the book for details (49-55, 169) 8. Missing Attribute Values: none 9. Class Distribution: 33% for each of 3 classes

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11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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