Przybyła-Kasperek Małgorzata, Kusztal Katarzyna
Institute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, Poland.
Entropy (Basel). 2022 Nov 4;24(11):1604. doi: 10.3390/e24111604.
The research concerns data collected in independent sets-more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables' cooperation and coalitions' formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification-the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity-a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.
该研究涉及在独立集合中收集的数据，更具体地说，是在局部决策表中收集的数据。管理这些数据的一种可能方法是基于每个表单独构建局部分类器。在文献中，可以找到许多用于组合独立分类器最终预测结果的方法，但在表的协作和联盟形成的研究方面投入的精力不足。这种方法的重要性体现在两个层面。首先，对分类质量的影响——为表的联盟构建组合分类器的能力应有助于学习更通用的概念。相应地，这应对新对象的分类质量产生影响。其次，将表组合成联盟将降低计算复杂度——构建的分类器数量将减少。本文提出了一种创建局部表联盟并为每个联盟生成聚合分类器的新方法。联盟是通过确定局部表中出现的属性值的某些特征并应用帕夫拉克冲突分析模型来生成的。在该研究中，基于一个联盟的聚合表构建了具有基尼指数的分类和回归树。该系统具有层次结构，因为在下一阶段，联盟分类器生成的决策使用多数投票进行聚合。将所提出系统的分类质量与不使用局部数据协作和联盟创建的方法进行了比较。该系统的结构是并行的，并且为局部表独立构建决策树。本文表明，所提出的方法在分类质量和执行时间方面有显著提高。威尔科克森检验证实，所提出方法获得的结果与无联盟方法获得的结果在准确率上的差异是显著的，显著性水平 = 0.005。所提出方法和无联盟方法获得的平均准确率值分别为：0.847 和 0.812；因此差异相当大。此外，实现所提出方法的算法比实现不使用联盟方法的算法执行速度快达 21 倍。
