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Titlebook: Evolutionary Computation in Data Mining; Ashish Ghosh,Lakhmi C. Jain Book 2005 Springer-Verlag Berlin Heidelberg 2005 Data mining.Evolutio

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楼主: crusade
发表于 2025-3-23 12:35:13 | 显示全部楼层
An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts,these modules, the rule-based evaluation criteria are designed in our mechanism. From our experiments, applying evolutionary algorithm to select critical financial ratios obtains better forecasting accuracy, and, a much better accuracy is obtained if more function modules are integrated in our mechanism.
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German Energy Policy in Transition,consideration to hidden relationships between features. A Genetic Algorithm is used to determine which set of features is the most predictive. Using ten well-known data sets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases.
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Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining,ection algorithms applied in different size data sets to evaluate the scaling up problem. The results show that the stratified evolutionary instance selection algorithms consistently outperform the non-evolutionary ones. The main advantages are: better instance reduction rates, higher classification accuracy and reduction in resources consumption.
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1434-9922 lutionary computation can be used for solving real-life probData mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, whi
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Louka T. Katseli,Nicholas P. Glytsoss members plays a key role in minimizing the combined bias and variance of the ensemble. In this chapter, we compare between different mechanisms and methods for promoting diversity in an ensemble. In general, we found that it is important to design the diversity promoting mechanism very carefully for the ensemble’s performance to be satisfactory.
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