Catálogo de publicaciones - libros

We describe extensions to the science querying system Scilog to enable it to make efficient simulators. Given a scientific model’s details and assumptions Scilog can create a C++ simulator to check assumption consistency. We used extended Scilog to test claims that Intelligent Design makes about Evolution and found them to be at odds with basic Biology.

Incremental learning is a good approach for classification when data-sets are too large or when new examples can arrive at any time. Forgetting these examples while keeping only the relevant information lets us reduce memory requirements. The algorithm presented in this paper, called IADEM, has been developed using these approaches and other concepts such as Chernoff and Hoeffding bounds. The most relevant features of this new algorithm are: its capability to deal with datasets of any size for inducing accurate trees and its capacity to keep updated the estimation error of the tree that is being induced. This estimation of the error is fundamental to satisfy the user requirements about the desired error in the tree and to detect noise in the datasets.

Given the recent breakthrough in constraint-based mining of local patterns, we decided to investigate its impact on feature construction for classification tasks. We discuss preliminary results concerning the use of the so-called -free sets. Our guess is that their minimality might help to collect important features. Once these sets are computed, we propose to select the essential ones w.r.t. class separation and generalization as new features. Our experiments have given encouraging results.

Paleoclimatology requires the analysis of paleo time-series, obtained from a number of independent techniques. Analytical reasoning techniques that combine the judgment of paleoceanographers with automated reasoning techniques are needed to gain deep insights about complex climatic phenomena. This paper presents an interactive visual analysis method based in Parallel Coordinates that enables the discovery of unexpected relationships and supports the reconstruction of climatic conditions of the past.

We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.

Gene selection is a common task in microarray data classification. The most commonly used gene selection approaches are based on gene ranking, in which each gene is evaluated individually and assigned a discriminative score reflecting its correlation with the class according to certain criteria, genes are then ranked by their scores and top ranked ones are selected. Various discriminative scores have been proposed, including t-test, S2N,RelifF, Symmetrical Uncertainty and-statistic. Among these methods, some require abundant data and require the data follow certain distribution, some require discrete data value. In this work, we propose a gene ranking method based on Grey Relational Analysis (GRA) in grey system theory, which requires less data, does not rely on data distribution and is more applicable to numerical data value. We experimentally compare our GRA method with several traditional methods, including Symmetrical Uncertainty, -statistic and ReliefF. The results show that the performance of our method is comparable with other methods, especially it is much faster than other methods.