Interactive Search of Rules in Medical Data Using Multiobjective Evolutionary Algorithms

ABSTRACT In this work, we propose an approach for evolving rules from medical data based on an interactive multi-criteria evolutionary search: besides selecting the set of criteria and the sets of potential antecedent and consequent attributes, the user can also intervene in the searching process by marking the uninteresting rules. The marked rules are further used in estimating a supplementary optimization criterion which expresses the user's opinion on the rule quality and is taken into account in the evolutionary process

Auto-completion of Contours in Sketches, Maps and Sparse 2D Images Based on Topological Persistence

We design a new fast algorithm to automatically complete closed contours in a finite point cloud on the plane. The only input can be a scanned map with almost closed curves, a hand-drawn artistic sketch or any sparse dotted image in 2D without any extra parameters. The output is a hierarchy of closed contours that have a long enough life span (persistence) in a sequence of nested neighborhoods of the input points. We prove theoretical guarantees when, for a given noisy sample of a graph in the plane, the output contours geometrically approximate the original contours in the unknown graph

Dealing with noise in ant-based clustering

Abstract- Separating the noise from data in a clustering process is an important issue in practical applications. Various algorithms, most of them based on density func-tions approaches, have been developed lately. The aim of this work is to analyze the ability of an ant-based clus-tering algorithm (AntClust) to deal with noise. The basic idea of the approach is to extend the information carried by an ant with an information concerning the density of data in its neighborhood. Experiments on some syn-thetic test data suggest that this approach could ensure the separation of noise from data without significantly increasing the algorithm’s complexity.