Titlebook: Artificial Intelligence and Innovations 2007: From Theory to Applications; Proceedings of the 4 Christos Boukis,Aristodemos Pnevmatikakis,L

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Genome

Armaan K. Malhotra,Mark Bernsteinnical and genomic data. Via the appropriate customization of standard medical and genomic data-models HealthObs achieves the semantic homogenization of remote clinical and gene-expression records, and their uniform XML-based representation. The system utilizes data-mining techniques (association rul

Irksome

Oliver L. Richards,George M. Ibrahimt. Sensors equipped with accelerometers are attached on the body of the patients and transmit patient movement data wirelessly to the monitoring unit. The methodology of support Vector Machines is used for precise classification of the acquired data and determination of a fall emergency event. Then

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ALT

An Evolving Oblique Decision Tree Ensemble Architecture for Continuous Learning Applicationsuous learning, the classification system classifies new instances for which after a short while the true class label becomes known and the system then receives this feedback control to improve its future predictions. We propose oblique decision trees as base classifiers using Support Vector Machines

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Clustering Improves the Exploration of Graph Mining Resultsof these graphs. Each graph can be seen as a transaction, or as a molecule — as the techniques applied in this paper are used in (bio)chemical analysis..In this work we will discuss an application that enables the user to further explore the results from a frequent subgraph mining algorithm. Such an

DEAWL

Robustness of learning techniques in handling class noise in imbalanced datasets, but more interesting class. A classifier induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. Many research efforts have been made to deal with class noise but none of them was designed for imbalanced datasets. This pa

occult

A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Setss paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed wrapper for reweighting training instances and it concludes that such a framework can be a more valuable

土產(chǎn)

Solving Traveling Salesman Problem Using Combinational Evolutionary Algorithmd problems and we found out the new method can works properly in problems based on permutation. We compare our results by the previous algorithms and show that our algorithm needs less time in comparison with known algorithms and so efficient for such problems.