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Ford Classification Challenge Results

Ford Classification Challenge Results. Mahmoud Abou-Nasr. The Challenge. Data samples from an automotive subsystem were collected in batches of 500 samples per diagnostic session.

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Ford Classification Challenge Results

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  1. Ford Classification Challenge Results Mahmoud Abou-Nasr WCCI2008: Ford Classification Challenge

  2. TheChallenge • Data samples from an automotive subsystem were collected in batches of 500 samples per diagnostic session. • The objective is a classifier that will determine whether a certain symptom exists or does not exist after examining the samples. • To this end, batches of 500 samples were collected when the symptom exists and batches of 500 samples were collected when the symptom does not exist. WCCI2008: Ford Classification Challenge

  3. About the Data • The 500 samples collected in each diagnostic session represent a set of sequential values of the measured variable, where sample n+1 occurs after sample n. • The beginning of the sampling process is not aligned with any external circumstance or any aspect of the observed pattern. WCCI2008: Ford Classification Challenge

  4. The Data Sets • Ford_A Data samples of known and hidden classification were collected in typical operating conditions, with minimal noise contamination. • Ford_B Data samples of known classification were collected in typical operating conditions, while datasamples of hidden classification were collected under noisy conditions. WCCI2008: Ford Classification Challenge

  5. Example of a Symptom Free Pattern WCCI2008: Ford Classification Challenge

  6. A Pattern that Exhibits the Symptom WCCI2008: Ford Classification Challenge

  7. What was provided to the Contestants? WCCI2008: Ford Classification Challenge

  8. Evaluation • The classification performance used in the evaluation is the accuracy of the classifier and in case of a tie the false positive rate of the classifier is also used. WCCI2008: Ford Classification Challenge

  9. Definitions Accuracy = (a + d) / (a + b + c +d) (1) False positive rate = b / (a + b) (2) WCCI2008: Ford Classification Challenge

  10. Ford_A Results WCCI2008: Ford Classification Challenge

  11. Ford_A Results WCCI2008: Ford Classification Challenge

  12. Ford_B Results WCCI2008: Ford Classification Challenge

  13. Ford_B Results WCCI2008: Ford Classification Challenge

  14. Results Both Data Sets: Ford_A and Ford_B WCCI2008: Ford Classification Challenge

  15. Results Both Data Sets: Ford_A and Ford_B WCCI2008: Ford Classification Challenge

  16. In Conclusion • This challenge problem was motivated by a potential automotive application. Abstractly, this problem amounts to classification of finite data sequences, in contrast to the more commonly encountered problem of classification based on feature vectors. • The length of the sequences reflects the time available for making the classification decision. Presumably, the task would be easier if the sequence length were increased, but this would violate the requirements of the application. • This problem does not appear to have a simple solution that emerges from visual inspection of the sequences. This distinguishes it from others in our experience where, at least in some range of operation, examples from opposite classes are readily differentiated. WCCI2008: Ford Classification Challenge

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