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Interactive Machine Learning Appendix
The document discusses several examples of interactive machine learning systems presented at conferences, including CueFlik which allows users to create rules for image ranking, CueT which helps triage alarms, Apolo which aids exploration of network data, and Visual-FSSEM which guides unsupervised feature selection. These systems incorporate user input through active learning, labeling examples, modifying parameters, and selecting feature subsets to iteratively update machine learning models.
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Interactive Machine Learning Appendix
- 1. Department of ComputerScience Interactive Machine Learning (Appendix) Zitao Liu ztliu@cs.pitt.edu School of Arts of and Sciences Department of Computer Science
- 2. Department of ComputerScience Examples: CueFlik(CHI2008) •Goal: allows users to quickly create their own rules for re- ranking images based on their visual characteristics. •Workflow: user provides CueFlik with examples of images that each rule should match, is shown the rule that CueFlik has learned, and then continues to provide CueFlik with examples of images that should be matched or rejected until they are satisfied with the learned rules. •ML Model: Nearest Neighbor; similarity measure; active learning. •Specialties: None.
- 3. Department of ComputerScience Examples: CueFlik(CHI2008) Positive examples Negative examples
- 4. Department of ComputerScience Examples: CueT(CHI2011) •Goal: learns from the triaging decisions of operators and help them quickly and accurately triage alarms in a highly dynamic environment. •Workflow: constantly updating and learning: alarms arrive, CueT provides recommendations and operators make decisions and CueT learns from it. •ML Model: Nearest Neighbor; Adaptive similarity measure (online metric learning). •Specialties: When to generate a new ticket(class)
- 5. Department of ComputerScience Examples: CueT(CHI2011) Alarm view Ticket view Severity Similarity visualization
- 6. Department of ComputerScience Examples: Apolo(CHI2011) •Goal: guides the user to incrementally and interactively explore large network data(demonstrated by scientific literature). •Workflow: iterative exploration: browse examples -> create groups(labels) -> label examples -> browse new examples… •ML Model: belief propagation. •Specialties: None.
- 7. Department of ComputerScience Examples: Apolo(CHI2011)
- 8. Department of ComputerScience Examples: Info. Extraction(CHI2009) •Goal: proposes a novel synergistic method for jointly amplifying community content creation and learning based information extraction. •Workflow: automatically extracts potential infobox and invites visitors to manually examined and explicitly confirmed it. •ML Model: Doc/Sentence level classifiers; Information Extraction(CRF). •Specialties: None.
- 9. Department of ComputerScience Examples: Info. Extraction(CHI2009) info. box dialog confirmation
- 10. Department of ComputerScience Examples: EnsembleMatrix(CHI2009) •Goal: presents a graphical view of confusion matrices to help users understand relative merits of various classifiers and helps users build combination multiclass classifiers. •Workflow: users can directly manipulate the visualizations in order to build combination multiclass classifiers(Partition and Reweight). •ML Model: ensemble classifier. •Specialties: none.
- 11. Department of ComputerScience Examples: EnsembleMatrix(CHI2009) Main ensemble classifier each classifier linear combination widget Heat map representation
- 12. Department of ComputerScience Examples: ManiMatrix(CHI2010) •Goal: provides controls and visualizations that enable system builders to refine the behavior of classification systems in an intuitive manner. •Workflow: users refine parameters of a confusion matrix (modify decision boundaries of classifiers) via an interactive cycle of reclassification and visualization. •ML Model: assigning sets of costs of misclassification in a confusion matrix. Costs of misclassification are non-uniform across classes. •Specialties: decision-theoretic analysis(푅푖푠푘푗= 푝푖⋅퐶표푠푡푖푗 푐푖 =1)
- 13. Department of ComputerScience Examples: ManiMatrix(CHI2010) 6 cloudy days were misclassified as rainy changes after user’s click lock the cell with a desired direction desired direction of the value changes (increasing)
- 14. Department of ComputerScience Examples: ReGroup(CHI2012) •Goal: helps people create custom, on-demand groups in online social networks. •Workflow: ReGroup observes a person’s normal interaction of adding members to a group, it learns a probabilistic model of group membership in order to suggest both additional members and group characteristics for filtering a friend list. It continually update its membership model based on interactive user feedback. •ML Model: Naïve Bayes(probability of each friend being a member of the group). Re-trained every time a person adds friends to a group. •Specialties: obtain implicit negative examples; unlearnable groups; missing data
- 15. Department of ComputerScience Examples: ReGroup(CHI2012) Top 5 relevant group characteristics Relevant friend suggestions A static, hierarchical list of all feature value filters
- 16. Department of ComputerScience Examples: Visual-FSSEM(KDD2000) •Goal: guides the feature selection procedures and enable a deeper understanding of the data in unsupervised setting. •Workflow: performs a greedy sequential forward or backward search for features as measured by the chosen performance criterion(scatter separability, maximum likelihood, cluster entropy and probability of error) Users can select initial feature subset and limit the pool features to search from. The user selects the direction forward or backward in every step and the number of steps. •ML Model: LDA(linear discriminant analysis) for visualization; mixture multivariate Gaussians for clustering. •Specialties: EM local minima;