Advances in Knowledge Discovery and Data Mining: 16th by Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay PDF

By Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)

ISBN-10: 3642302165

ISBN-13: 9783642302169

ISBN-10: 3642302173

ISBN-13: 9783642302176

The two-volume set LNAI 7301 and 7302 constitutes the refereed complaints of the sixteenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in could 2012. the whole of 20 revised complete papers and sixty six revised brief papers have been conscientiously reviewed and chosen from 241 submissions. The papers current new principles, unique learn effects, and sensible improvement reviews from all KDD-related parts. The papers are prepared in topical sections on supervised studying: lively, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on development mining: networks, graphs, time-series and outlier detection, and knowledge manipulation: pre-processing and measurement relief within the moment volume.

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From a wide range of model classes) and ensembles that use various temporal weighting parameters. In all cases, these ensembles are more robust and increase the accuracy over more traditional ensemble techniques (and single classifiers). 05 with a 16% reduction in error, justifying the proposed temporal ensemble methodologies. In the next experiment, we construct enTVRC RPT sembles using the feature classes. We use the DT primitive models (with the transformed feature space) in order to investigate (more accurately) the most significant feature class (communication, team, centrality, topics) and also to identify the minimum amount of temporal information required to outperform relational ensembles.

Edu Abstract. In this paper, we introduce several approaches for maintaining weights over the aggregate skill ratings of subgroups of teams during the skill assessment process and extend our earlier work in this area to include game-specific performance measures as features alongside aggregate skill ratings as part of the online prediction task. We find that the inclusion of these game-specific measures do not improve prediction accuracy in the general case, but do when competing teams are considered evenly matched.

For LIBLINEAR, there are primarily two parameters C and W that will affect the performance. C is the penalty coefficient for training errors and W balances the penalty on the two classes. In our experiment, we set C = 1000 and W as the negative class proportion. 9. The purpose is to give more penalty to the error on the minority class. For active learning, due to the simplicity and effectiveness of Uncertainty Sampling 8 , we adopt uncertainty sampling as the strategy to select the informative examples from the unlabeled pool.

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Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I by Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)


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