Titlebook: Machine Learning and Data Mining in Pattern Recognition; 7th International Co Petra Perner Conference proceedings 2011 Springer-Verlag GmbH

B-cell

Smoothing Multinomial Na?ve Bayes in the Presence of Imbalancepared to known methods of smoothing, and is the only method tested that performs well regardless of the type of text preprocessing used. It is particularly effective compared to existing methods when the data is imbalanced.

迅速成長

GENCCS: A Correlated Group Difference Approach to Contrast Set Miningn and all confidence to select the attribute-value pairs that are most highly correlated, in order to mine CGDs. Our experiments on real datasets demonstrate the efficiency of our approach and the interestingness of the CGDs discovered.

LAP

abreast

Boosting Inspired Process for Improving AUCs much computation time in the training process. Our experiment results show that the new boosting algorithm . does improve ranking performance of AdaBoost when the base learning algorithm is the improved ranking favored decision tree C4.4 or na?ve Bayes.

Hirsutism

Exterior

Decisions: Algebra and Implementationthe decision algebra operations efficiently and capture classification information in a non-redundant way. Compared to classical decision tree implementations, decision graphs gain learning and classification speed up to 20% without accuracy loss and reduce memory consumption by 44%. This is confirmed by experiments.

混沌

尾隨

Parameter-Free Anomaly Detection for Categorical Data. In this paper, we propose a formal definition of outliers and formulize outlier detection as an optimization problem. To solve the optimization problem, we design a practical and parameter-free method, named ITB. Experimental results show that the ITB method is much more effective and efficient than existing main-stream methods.

傷心

Nonporous