Storage Retrieval

Download Advanced Data Mining and Applications: 12th International by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng PDF

By Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng

This publication constitutes the lawsuits of the twelfth foreign convention on complicated info Mining and purposes, ADMA 2016, held in Gold Coast, Australia, in December 2016.

The 70 papers awarded during this quantity have been conscientiously reviewed and chosen from a hundred and five submissions. the chosen papers lined a wide selection of vital themes within the sector of knowledge mining, together with parallel and dispensed facts mining algorithms, mining on information streams, graph mining, spatial information mining, multimedia info mining, internet mining, the web of items, wellbeing and fitness informatics, and biomedical info mining.

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Extra info for Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings

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The aim of the bipartite ranking algorithm is to maximize the Area Under the Curve (AUC) by learning a function that scores positive instances higher than negative instances. Therefore, the optimization problem of such a ranking model is formulated as the minimization of a pairwise loss function. This ranking problem can be solved by applying a binary classifier to pairs of positive and negative c Springer International Publishing AG 2016 J. Li et al. ): ADMA 2016, LNAI 10086, pp. 35–49, 2016. 1007/978-3-319-49586-6 3 36 M.

2015, cached: http://bit. ly/1PPbeSv. Textual Cues for Online Depression in Community and Personal Settings 23 these two feature sets in classifying a blog post into one of two target classes. Given a document d ∈ B, we predict if the document belongs to a Community or Personal blog based on the textual features x(d) . We are interested in not only which sets of features perform well in the classification but also which features in the sets are strongly predictive of depression. For this purpose, the least absolute shrinkage and selection operator (Lasso) [9], a regularized regression, is chosen.

Prediction models of community (versus personal) posts using topics and language styles as features. Features in red are positive predictors of community posts whilst the blues are the negatives. Individual coefficients that were not significant have been omitted, as have topics and LIWC features with no significant coefficients. (Color figure online) 32 T. Nguyen et al. Table 6. Topics with high weights in the prediction of community posts (versus personal ones). Positive weights are in red and negatives are in blue.

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