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 court cases of the sixteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in may possibly 2012. the entire 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 useful improvement reports from all KDD-related parts. The papers are geared up in topical sections on supervised studying: energetic, 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 size aid within the moment volume.
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Extra resources 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 prediction tasks are to predict one of seven machine learning papers and to predict AI papers given the topic of its references. In addition, these techniques are evaluated using the most prevalent topics its authors are working on through collaborations with other authors. 2 Temporal Models The space of temporal-relational models are evaluated using a representative sample of classiﬁers with varying temporal weightings and granularities. , some set of timesteps) and apply the model to Dt+1 .
Can we reduce the number of labeled examples signiﬁcantly? To tackle the lack of labeled examples, active learning can be a good choice [16,12,18]. , human expert). Usually, those most informative examples can beneﬁt the classiﬁcation performance most. Several works have successfully applied active learning in text classiﬁcation [16,5,20]. However, to our best knowledge, no previous works have been done in hierarchical text classiﬁcation with active learning due to several technical challenges.
Additionally, the topic attributes are shown to be the most useful for the temporal ensembles (Fig. 7), indicating the utility of using topics to understand the context and strength of relationships. 7 Conclusion We proposed and validated a framework for temporal-relational classiﬁers, ensembles, and more generally, representations for temporal-relational data. We evaluated an illustrative set of temporal-relational models from the proposed framework. Empirical results show that the models signiﬁcantly outperform competing classiﬁcation models that use either no temporal information or a very limited amount.