By Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)
The two-volume set LNAI 7301 and 7302 constitutes the refereed lawsuits of the sixteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in may well 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 examine effects, and sensible improvement reports 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 aid within the moment volume.
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Additional info for Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I
The main ﬁndings are summarized below: Temporal-relational models signiﬁcantly outperform relational and nonrelational models. The classes of temporal-relational models each have advantages and disadvantages in terms of accuracy, eﬃciency, and interpretability. Models based strictly on temporal granularity are more interpretable but less accurate than models that learn the temporal inﬂuence. The more complex models that combine both are generally more accurate, but less eﬃcient. Temporal ensemble methods signiﬁcantly outperform non-relational and relational ensembles.
Hierarchical text classiﬁcation plays an important role in many real-world applications, such as webpage topic classiﬁcation, product categorization and user feedback classiﬁcation. Usually a large number of training examples are needed to build an accurate hierarchical classiﬁcation system. Active learning has been shown to reduce the training examples signiﬁcantly, but it has not been applied to hierarchical text classiﬁcation due to several technical challenges. In this paper, we study active learning for hierarchical text classiﬁcation.
In this paper, we build on our previous work by introducing ﬁve algorithms which address this drawback in various ways, TeamSkill-AllK-Ev-OL1 (OL1), TeamSkill-AllK-Ev-OL2 (OL2), TeamSkill-AllK-Ev-OL3 (OL3), TeamSkill-AllKEVGen (EVGen), and TeamSkill-AllK-EVMixed (EVMixed). The ﬁrst three OL1, OL2, and OL3 - employ adaptive weighting frameworks to adjust the summation weights for each n-sized group skill rating and limit their feature set to data common across all team games: the players, team assignments, and the outcome of the game.