Fundamenta Informaticae - Volume 113, issue 2

Authors: Ramon, Jan | Costa, Fabrizio | Florêncio, Christophe Costa | Kok, Joost

Article Type: Research Article

DOI: 10.3233/FI-2011-601

Citation: Fundamenta Informaticae, vol. 113, no. 2, pp. i-ii, 2011

Authors: Arevalillo, Jorge M | Navarro, Hilario

Article Type: Research Article

Abstract: Random Forests (RF) is an ensemble technology for classification and regression which has become widely accepted in the bioinformatics community in the last few years. Its predictive strength, along with some of the utilities, rich in information, provided by the output, has made RF an efficient data mining tool for discovering patterns in high dimensional data. In this paper we propose a search strategy that explores a subset of the input space in an exhaustive way using RF as the search engine. Our procedure begins by taking the variables previously rejected by a sequential search procedure and uses the out …of bag error rate of the ensemble, obtained when trained over an augmented data set, as criterion to capture difficult to uncover bivariate patterns associated with an outcome variable. We will show the performance of the procedure in some synthetic scenarios and will give an application to a real microarray experiment in order to illustrate how it works for gene expression data. Show more

Keywords: Bivariate interactions, random forests, high dimensional data

DOI: 10.3233/FI-2011-602

Citation: Fundamenta Informaticae, vol. 113, no. 2, pp. 97-115, 2011

Authors: Hämäläinen, Wilhelmiina

Article Type: Research Article

Abstract: Dependency analysis is one of the central problems in bioinformatics and all empirical science. In genetics, for example, an important problem is to find which gene alleles are mutually dependent or which alleles and diseases are dependent. In ecology, a similar problem is to find dependencies between different species or groups of species. In both cases a classical solution is to consider all pairwise dependencies between single attributes and evaluate the relationships with some statistical measure like the χ2 -measure. It is known that the actual dependency structures can involve more attributes, but the existing computational methods are too inefficient …for such an exhaustive search. In this paper, we introduce efficient search methods for positive dependencies of the form X → A with typical statistical measures. The efficiency is achieved by a special kind of a branch-and-bound search which also prunes out redundant rules. Redundant attributes are especially harmful in dependency analysis, because they can blur the actual dependencies and even lead to erroneous conclusions. We consider two alternative definitions of redundancy: the classical one and a stricter one. We improve our previous algorithm for searching for the best strictly non-redundant dependency rules and introduce a totally new algorithm for searching for the best classically non-redundant rules. According to our experiments, both algorithms can prune the search space very efficiently, and in practice no minimum frequency thresholds are needed. This is an important benefit, because biological data sets are typically dense, and the alternative search methods would require too large minimum frequency thresholds for any practical purpose. Show more

Keywords: statistical dependence, redundancy, χ$^{2}$-measure, z-score, search algorithms

DOI: 10.3233/FI-2011-603

Citation: Fundamenta Informaticae, vol. 113, no. 2, pp. 117-150, 2011

Authors: Cilia, Elisa | Landwehr, Niels | Passerini, Andrea

Article Type: Research Article

Abstract: We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory …features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered. Show more

DOI: 10.3233/FI-2011-604

Citation: Fundamenta Informaticae, vol. 113, no. 2, pp. 151-177, 2011