Ying Yang

According to our database1, Ying Yang
  • authored at least 32 papers between 2001 and 2017.
  • has a "Dijkstra number"2 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2017
Measurement Scales.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Discretization.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

2010
Measurement Scales.
Proceedings of the Encyclopedia of Machine Learning, 2010

Discretization.
Proceedings of the Encyclopedia of Machine Learning, 2010

Discretization Methods.
Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

2009
Adapted One-versus-All Decision Trees for Data Stream Classification.
IEEE Trans. Knowl. Data Eng., 2009

Discretization for naive-Bayes learning: managing discretization bias and variance.
Machine Learning, 2009

Anytime classification for a pool of instances.
Machine Learning, 2009

Flexible decision tree for data stream classification in the presence of concept change, noise and missing values.
Data Min. Knowl. Discov., 2009

A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2009

2008
A lazy bagging approach to classification.
Pattern Recognition, 2008

Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams.
Fundam. Inform., 2008

Detecting intrusion transactions in databases using data item dependencies and anomaly analysis.
Expert Systems, 2008

Conceptual equivalence for contrast mining in classification learning.
Data Knowl. Eng., 2008

2007
To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators.
IEEE Trans. Knowl. Data Eng., 2007

Classifying under computational resource constraints: anytime classification using probabilistic estimators.
Machine Learning, 2007

To Better Handle Concept Change and Noise: A Cellular Automata Approach to Data Stream Classification.
Proceedings of the AI 2007: Advances in Artificial Intelligence, 2007

2006
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection.
Knowl. Inf. Syst., 2006

Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams.
Data Min. Knowl. Discov., 2006

To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles.
Proceedings of the Machine Learning: ECML 2006, 2006

Incremental Discretization for Naïve-Bayes Classifier.
Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006

2005
Combining proactive and reactive predictions for data streams.
Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

Ensemble Selection for SuperParent-One-Dependence Estimators.
Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005

Discretization Methods.
Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

2004
Parameter Tuning for Induction-Algorithm-Oriented Feature Elimination.
IEEE Intelligent Systems, 2004

Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources.
Proceedings of the Knowledge Discovery in Databases: PKDD 2004, 2004

Dynamic Classifier Selection for Effective Mining from Noisy Data Streams.
Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004

Error Detection and Impact-Sensitive Instance Ranking in Noisy Datasets.
Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004

2003
Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2003

On Why Discretization Works for Naive-Bayes Classifiers.
Proceedings of the AI 2003: Advances in Artificial Intelligence, 2003

2002
Non-Disjoint Discretization for Naive-Bayes Classifiers.
Proceedings of the Machine Learning, 2002

2001
Proportional k-Interval Discretization for Naive-Bayes Classifiers.
Proceedings of the Machine Learning: EMCL 2001, 2001


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