Abstract-Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice. This paper presents a comparative study of three ensemble methods (TreeBagger, LPBoost and Subspace) using a clinical dataset with 25% missing values to predict the overall survival of women with breast cancer. To complete the absent values, the k-nearest neighbor (k-NN) algorithm was used with four distinct neighbor values, trying to determine the best one for this particular scenario. Tests were performed for each of the three ensemble methods and each k-NN configuration, and their performance compared using a Friedman test. Despite the complexity of this challenge, the produced results are promising and the best algorithm configuration (TreeBagger using 3 neighbors) presents a prediction accuracy of 73%.
Representing large amounts of flows involves dealing with the representation of directionality and the reduction of visual cluttering. This article describes the application of two flow representation techniques to the visualization of transitions of customers among supermarkets over time. The first approach relies in arc representations together with a combination of methods to represent directionality of transitions. The other approach uses a swarm-based system in order to reduce visual clutter, bundling edges in an organic fashion and improving clarity.
Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.
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