Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2015)
The three preceding editions of the FCA4AI Workshop showed that many researchers working in Artificial Intelligence are deeply interested by a well-founded method for classi- fication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). The first edition of FCA4AI was co-located with ECAI 2012 in Montpellier and published as http://ceur-ws.org/Vol-939/, the second edition was co-located with IJCAI 2013 in Beijing and published as http://ceur-ws.org/Vol-1058/, and finally the third edition was co-located with ECAI 2014 in Prague and published as http://ceur-ws.org/Vol-1257/. Based on that, we decided to continue the series and we took the chance to organize a new edition of the workshop in Buenos Aires at the IJCAI 2015 Conference. This year, the workshop has again attracted many different researchers working on actual and important topics, e.g. recommendation, linked data, classification, biclustering, pattern mining, ontology design, and various applications. This shows the diversity and the richness of the relations between FCA and AI. Moreover, this is a good sign for the future and especially for young researchers that are at the moment working in this area or who will do.
In this paper, we introduce a modication of the lazy associative classication which addresses the graph classication problem. To deal with intersections of large graphs, graph intersections are approximated with all common subgraphs up to a xed size similarly to what is done with graphlet kernels. We illustrate the algorithm with a toy example and describe our experiments with a predictive toxicology dataset.
Pattern structures allow one to approach the knowledge extraction problem in case of arbitrary object descriptions. They provide the way to apply Formal Concept Analysis (FCA) techniques to non-binary contexts. However, in order to produce classification rules a concept lattice should be built. For non-binary contexts this procedure may take much time and resources. In order to tackle this problem, we introduce a modication of the lazy associative classification algorithm and apply it to credit scoring. The resulting quality of classification is compared to existing methods adopted in bank systems.
We propose a new algorithm for recommender systems with numeric ratings which is based on Pattern Structures (RAPS). As the input the algorithm takes rating matrix, e.g., such that it contains movies rated by users. For a target user, the algorithm returns a rated list of items (movies) based on its previous ratings and ratings of other users. We compare the results of the proposed algorithm in terms of precision and recall measures with Slope One, one of the state-of-theart item-based algorithms, on Movie Lens dataset and RAPS demonstrates the best or comparable quality.