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Optimizing Parameters Values of Tree-Based Contrast Subspace Miner using Genetic Algorithm

机译：利用遗传算法优化基于树对比子空间矿工的参数值

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Mining contrast subspace finds contrast subspaces or subspaces where a query object is most similar to a target class but different from other class in a two-class multidimensional data set. Tree-based contrast subspace miner (TB-CSMiner) which employs tree-based likelihood contrast scoring function has been recently introduced to mine contrast subspaces of a query object by constructing tree from a subspace that is data objects in a subspace space are divided into two nodes recursively with respect to the query object until the node contains only objects of same class or a minimum number of objects. A query object should fall in the node that has higher number of objects belong to the target class against the other class in a contrast subspace. The effectiveness of TB-CSMiner in finding contrast subspace of a query object relies on the values of several parameters involved which include the minimum number of objects in a node, the denominator of tree-based likelihood contrast scoring function, the number of relevant features for tree construction, and the number of random subspaces for contrast subspace search. It is difficult to identify the values of these parameters in a straightforward way based on the conventional analysis. As a consequence, this paper proposes a genetic algorithm based method for identifying the parameters values of TB-CSMiner in which sets of parameters values are treated as individuals and evolved to return the best set of parameters values. The experiment results show that the TB-CSMiner with parameters values identified through the genetic algorithm outperformed those identified through the conventional analysis in most of the cases.
机译：挖掘对比度子空间发现对比度的子空间的子空间或其中一个查询对象是在两类多维数据集最相似的一个目标类，但来自其他类不同。基于树的对比度子空间矿工（TB-CSMiner），其采用基于树的似然对比度打分函数已经通过从子空间这是在一个子空间空间数据对象被分成两个构建树是新加到查询对象的矿井对比度子空间节点递归地相对于所述查询对象，直到节点只包含同一类或对象的最小数目的对象。查询对象应落在具有属于针对在造影子空间中的其它类中的目标类更高数量的对象的节点。 TB-CSMiner中找到查询对象的对比度子空间的有效性依赖于所涉及的几个参数，其中包括对象中的节点的最小数量，基于树的似然对比度打分函数的分母，的相关特征的数的值树构建，并对比子空间搜索随机子空间的数量。这是难以识别在基于常规分析一个简单的方法，这些参数的值。因此，提出了用于识别在哪个组参数的值的被视为个人和进化到返回参数值最好的一组TB-CSMiner的参数的值的遗传算法为基础的方法。实验结果表明，TB-CSMiner通过所述遗传算法优于那些通过在大多数情况下，常规的分析，鉴定识别的参数值。

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