By Troels Andreasen, Henning Christiansen, Juan-Carlos Cubero, Zbigniew W. Ras

This publication constitutes the refereed court cases of the twenty first foreign Symposium on Methodologies for clever structures, ISMIS 2014, held in Roskilde, Denmark, in June 2014. The sixty one revised complete papers have been conscientiously reviewed and chosen from 111 submissions. The papers are geared up in topical sections on complicated networks and information flow mining; facts mining tools; clever platforms purposes; wisdom illustration in databases and structures; textual information research and mining; distinctive consultation: demanding situations in textual content mining and semantic details retrieval; precise consultation: warehousing and OLAPing complicated, spatial and spatio-temporal info; ISMIS posters.

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Extra info for Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings

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To make the comparison more meaningful, we set the same parameter values for all the algorithms. For ensemble methods we set the number of component classifiers to 10: AUE, NSE, and Bag have ten Hoeffding Trees, and since AWE uses static learners it has ten J48 trees. We decided to use 10 component classifiers as this was the number suggested and tested in [9]. 1). 05 [6]. 1 Data Sets During our experiments we used 4 real and 8 synthetic datasets. The real datasets were the CSLOG documents, which consist of web logs categorized into two classes, as described in [1,9].

Moreover, since the classification model of XStreamClass depends strictly on the current set of frequent patterns, the algorithm has a forgetting model steered directly by adaptive pattern mining. This allows the proposed algorithm to react to changes as soon as they are visible in the patterns, in contrast to the compared algorithms, which would require a separate drift detection model. This was especially visible on the accuracy plot of the Gradual dataset presented in Fig. 2. One can see that around the 250k example accuracy slightly drops as gradual drift starts, but with time XStreamClass recovers from the drift.

Constrained tree inclusion. J. Discrete Alg. 3(2-4), 431–447 (2005) 14. : RTED: A robust algorithm for the tree edit distance. PVLDB 5(4), 334–345 (2011) 15. : MOA: Massive Online Analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010) 16. : Statistical comparisons of classifiers over multiple data sets. J. pl Abstract. Incremental learning of classification rules from data streams with concept drift is considered. We introduce a new algorithm RILL, which induces rules and single instances, uses bottom-up rule generalization based on nearest rules, and performs intensive pruning of the obtained rule set.

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