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[UCI Machine Learning Repository]
Samples
Input Output
Neural Nets
HMMs
Naive Bayesian](https://image.slidesharecdn.com/visualizingprobabilisticclassificationdatainweka-191128115625/85/Visualizing-probabilistic-classification-data-in-weka-4-320.jpg)
Uploaded byKinda Altarbouch
Visualizing probabilistic classification data in weka
This document discusses the design, implementation, and testing of a visual inspection tool for probabilistic classification data in Weka, supervised by Dr. Bilal Alsallakh. It highlights the tool's capability to analyze classifier behavior, improve classification performance, and guide future improvements through interactive exploration of classification results. The implementation is designed to be modular, facilitating integration with other data mining frameworks.
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Visualizing probabilistic classification data in weka
- 1. Visualizing Probabilistic Classification Datain WEKA Supervised by Dr. Bilal Alsallakh Designed, Implemented and Tested by Mohammed Saed Haj Ali Kinda Altarbouch F.I.T.E of Damascus, Syria – AI Department 2015
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- 4. Probabilistic Classification Data [UCIMachine Learning Repository] Samples Input Output Neural Nets HMMs Naive Bayesian
- 5. Data Mining andMachine Learning Platforms
- 6. Data Mining andMachine Learning Platforms
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- 10. Analyze Probability Distributions 100% 0 100% 0 100% 0 •High vs. Low discrimination (e.g. “4” vs. “8”) • high probability vs. correct classification (e.g. inconsistent for “5”) ProbabilityinProbabilityinProbabilityin
- 11. Analyze Probability Distributions 100% 0 100% 0 100% 0 •High vs. Low discrimination (e.g. “4” vs. “8”) • high probability vs. correct classification (e.g. inconsistent for “5”) ProbabilityinProbabilityinProbabilityin
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- 14. Can a datafeature separate correctly and incorrectly classified samples? Sort features by separability: • Median distance Group Separability Improvement > 8%
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- 17. Visual Inspection Correct ActualClass from C5 to C0
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- 19. Defining Post-Classification Rules Rectifycertain false negatives Rectify certain false positives
- 20. Conclusion Classification probabilities rich ofinformation explains classifier behavior Interactive exploration Reveals several insights Guides new improvements More work is needed! Improvement analysis, comparison, ... Future Works
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- 22. Defining Post-Classification Rules Rectifycertain false negatives Rectify certain false positives
- 23. Integration with FurtherData Mining Frameworks The implementation is modular and well-separated from WEKA Integration with further Java-based Frameworks (KNIME, RapidMiner) is straightforward
- 24. Conclusion Classification probabilities rich ofinformation explains classifier behavior Interactive exploration Reveals several insights Guides new improvements More work is needed! Improvement analysis, comparison, ... Conclusion
- 25. A Visual InspectionTool for Classification Data ▪ Integrated with WEKA ▪ Works with any classifier Helps in ▪ Undertanding classifier behavior ▪ Finding bugs in data and algorithms ▪ Improving classification performance Summary
- 26. Conclusion Classification probabilities rich ofinformation explains classifier behavior Interactive exploration Reveals several insights Guides new improvements More work is needed! Improvement analysis, comparison, ... Backup Slides
- 27. Task C: GroupSeparability Can a data feature separate correctly and incorrectly classified samples? Sort features by separability: • p-Value • F-measure
- 28. Defining Post-Classification Rules Usea different classifier for selected samples Neural Nets Majority vote Naïve Bayisian
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- 31. Task A: AnalyzeProbability Distributions
- 32. Separability by 2-features •E.g. X8 and Y3 • Open question
- 33. Multi-class Classification Sample Classes Classifier 72% 01 2 3 4 5 6 7 8 9
- 34. Probabilistic Classifiers Neural NetsHidden Markov Models Naive Bayesian
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