Workshop Details

RIKD: Workshop on Reliability Issues in Knowledge Discovery

Organized by Honghua Dai, James Liu, and Evgueni Smirnov
08:30 - 11:50
Room: Turner
http://www.deakin.edu.au/individuals-sites/?request=~hdai/RIKD12

The 2012 IEEE ICDM workshop ``Reliability Issues in Knowledge Discovery' aims at presenting the recent advances in the emerging field of reliable knowledge discovery from data. This year the workshop focus has shifted from theory and methods towards experimental studies and applications. The latter can be seen in the program consisting of 6 papers and an invited talk.

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08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov	08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov	08:30 - 08:40	Welcoming and Introduction	Welcoming and Introduction		08:30 - 08:40	Welcoming and Introduction Honghua Dai and Evgueni Smirnov
08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov	08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov	08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning		08:40 - 09:20	Invited Talk: Reliable Prediction of Survival of Cancer Patients using Multi-Centric Distributed Learning Georgi Nalbantov
09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai	09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai	09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation		09:20 - 09:40	A Weighted Support Vector Data Description based on Rough Neighborhood Approximation Yanxing Hu, James N. K. Liu, Yuan Wang and Lucas Lai
09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro	09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro	09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM	Bootstrap Confidence Intervals in DirectLiNGAM		09:40 - 10:00	Bootstrap Confidence Intervals in DirectLiNGAM Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, and Tatsuya Tashiro
10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	10:00 - 10:30	Coffee Break	Coffee Break		10:00 - 10:30	Coffee Break
10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan	10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan	10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer	Reliable Knowledge Discovery with A Minimal Causal Model Inducer		10:30 - 10:50	Reliable Knowledge Discovery with A Minimal Causal Model Inducer Honghua Dai, Sarah Johnston, and Min Gan
10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz	10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz	10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data		10:50 - 11:10	The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data Daniel Antwi, Herna Viktor, and Nathalie Japkowicz
11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West	11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West	11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification		11:10 - 11:30	Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification Abdul Nurunnabi and Geoff West
11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko	11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko	11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals	Model Selection with Combining Valid and Optimal Prediction Intervals		11:30 - 11:50	Model Selection with Combining Valid and Optimal Prediction Intervals Darko Pevec and Igor Kononenko