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One of the most important problems in data mining is how to manage a large amount of data and to extract efficient knowledge from large databases. Although many machine learning methods and statistical methods have been proposed to solve this problem, they are not powerful when we have more than 1000 samples, since the computational complexity of these algorithms is larger than or approximately equal...
In order to transfer mined knowledge for various datasets obtained from transferring situations, it is important to detect not only availability of transferring the knowledge but also detecting their limitations of the transfer. Although most of methods to detect the limitations use performance indices of sets of classifiers such as accuracies of classifier sets, those of each classifier are also...
In data mining post-processing, rule selection using objective rule evaluation indices is one of a useful method to find out valuable knowledge from mined patterns. However, the relationship between an index value and experts' criteria has never been clarified. In this study, we have compared the accuracies of classification learning algorithms for datasets with randomized class distributions and...
In datamining post-processing, rule selection using objective rule evaluation indices is one of a useful method to find out valuable knowledge from mined patterns. However, the relationship between an index value and expertspsila criteria has never been clarified. In order to determine the relationship, we have developed a method to obtain learning models from a dataset consisting of objective rule...
A new anomaly detection system for mobile handsets has been proposed. In this system, software behavior that deviates from a model representing normal behavior is considered to be an anomaly. It is generally impossible to cover software behavior exhaustively by the model, which could adversely affect accuracy. In order to resolve this problem, the proposed system assesses the anomalousness of behavior...
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