A classification algorithm based on explanation could be put into the category of heuristic classification in knowledge engineering.

基于解释的分类算法可纳入知识工程中启发式分类的范畴。

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We deal with the inconsistency through classification accuracy, using heuristic algorithms we can get a set of minimal productive rules satisfying the given classification accuracy.

通过分类正确度有效处理了决策表的不一致性，采用启发式算法，挖掘出满足给定精确度的最简产生式规则知识。

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This paper introduces the classification model of random decision tree and how to heuristic selected the depth and the number, the experiment shows that the algorithm is effectiveness and efficiency.

该文介绍了随机决策树分类模型及如何启发式选择随机决策树的深度及棵树，通过实验证明了该算法的有效性和高效性。

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