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Bringing Diverse Classifiers to Common Grounds: dtransform

Explore the innovative dtransform method by Devi Parikh and Tsuhan Chen from Carnegie Mellon University. This approach aims to bring classifiers to a common ground for improved comparisons and combinations, enhancing posterior probability estimation and boosting classification performance. By incorporating statistical properties and being independent of classifier type, dtransform paves the way for more meaningful analysis in multi-class scenarios. Dive into experiments showcasing its adaptability to synthetic and real datasets, demonstrating its superiority over traditional techniques like normalization and softmax. Discover how this parametric transformation can revolutionize classifier evaluations and decision-making processes in diverse classification problems.

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Bringing Diverse Classifiers to Common Grounds: dtransform

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  1. Bringing Diverse Classifiers to Common Grounds: dtransform Devi Parikh and Tsuhan Chen Carnegie Mellon University April 3, ICASSP 2008

  2. Outline • Motivation • Related work • dtransform • Results • Conclusion Motivation Related work dtransform Results Conclusion

  3. ~ c3 c3 ~ c2 c2 1 0 0 0.7 1 0 1 0.3 Motivation • Consider a three-class classification problem • Multi-layer perceptron (MLP) neural network classifier • Normalized outputs for a test instance • class 1: 0.5 • class 2: 0.4 • class 3: 0.1 • Which class do we pick? • If we looked deeper… Motivation Related work dtransform Results Conclusion class 1 Adaptability class 2 - examples + examples ~ c1 c1 0.6

  4. Motivation • Diversity among classifiers due to different • Classifier types • Feature types • Training data subset • Randomness in learning algorithm • Etc. • Bring to common grounds for • Comparing classifiers • Combining classifiers • Cost considerations • Goal: A transformation that • Estimates posterior probabilities from classifier outputs • Incorporates statistical properties of trained classifier • Is independent of classifier type, etc. Motivation Related work dtransform Results Conclusion

  5. Related work • Parameter tweaking • In two-class problems (biometric recognition), ROC curves are prevalent • Straightforward multi-class generalizations are not known • Different approaches for estimating posterior probabilities for different classifier types • Classifier type dependent • Do not adapt to statistical properties of classifiers post-training • Commonly used transforms: • Normalization • Softmax • Do not adapt Motivation Related work dtransform Results Conclusion

  6. dtransform Set-up: “Multiple classifiers system” • Multiple classifiers • One classifier with multiple outputs • Any multi-class classification scenario where classification system gives a score for each class Motivation Related work dtransform Results Conclusion

  7. 0 1 dtransform • For each output mc • Raw output tc maps to transformed output 0.5 • Raw output 0 maps to transformed output 0 • Raw output 1 maps to transformed output 1 • Monotonically increasing + examples - examples Motivation Related work dtransform Results Conclusion ~ c c mc tc

  8. dtransform Motivation Related work dtransform Results Conclusion 1 t = 0.1 t = 0.5 transformed output: D t = 0.9 raw output: m 0 1

  9. dtransform • Logistic regression • Two (not so intuitive) parameters to be set • Histogram itself • Non-parameteric: subject to overfitting • dtransform: just one intuitive parameter • Affine transform Motivation Related work dtransform Results Conclusion

  10. Experiment 1 • Comparison with other transforms • Same ordering, different values • Normalization and softmax  not adaptive • tsoftmax and dtransform  adaptive • Similar values, different ordering • softmax and tsoftmax Motivation Related work dtransform Results Conclusion

  11. Experiment 1 • Synthetic data • True posterior probabilities known • 3 class problem • MLP neural network with 3 outputs Motivation Related work dtransform Results Conclusion

  12. Experiment 1 • Comparing classification accuracies Motivation Related work dtransform Results Conclusion

  13. Experiment 1 • Comparing KL distance Motivation Related work dtransform Results Conclusion

  14. Experiment 2 • Real intrusion detection dataset • KDD 1999 • 5 classes • 41 features • ~ 5 million data points • Learn++ with MLP as base classifier • Classifier combination rules: • Weighted sum rule • Weighted product rule • Cost matrix involved Motivation Related work dtransform Results Conclusion

  15. Experiment 2 Motivation Related work dtransform Results Conclusion

  16. Conclusion • Parametric transformation to estimate posterior probabilities from classifier outputs • Straightforward to implement and gives significant classification performance boost • Independent of classifier type • Post-training • Incorporates statistical properties of trained classifier • Brings diverse classifiers to common grounds for meaningful comparisons and combinations Motivation Related work dtransform Results Conclusion

  17. Thank you! Questions? Motivation Related work dtransform Results Conclusion

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