Ensemble methods fabricate a sequence of classifiers for classifying fresh instances by procuring a weighted vote of their individual predictions. Toning down the error and increasing accuracy is an avant-garde problem in ensemble classification. This paper presents a novel generic object-oriented voting and weighting adapted stacking framework for utilizing an ensemble of classifiers for prediction. This universal framework operates based on the weighted average of the probabilities of any suite of base learners and the final prediction is the aggregate of their respective votes. For illustrative purposes, three familiar heterogeneous classifiers, such as the Support Vector Machine, k-Nearest Neighbor and Naïve Bayes, are utilized as candidates for ensemble classification using the proposed stacked framework. Further, the ensemble classifier built upon the framework is compared with others and evaluated using various cross-validation levels and percentage splits on a range of benchmark datasets. The outcome distinguishes the framework from the competition. The proposed framework is used to predict the crime propensity of prisoners most accurately, with 99.9901% accuracy.

中文翻译：

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异构分类算法集合的堆叠框架

集成方法制造一系列分类器，通过对它们的个体预测进行加权投票来对新实例进行分类。降低错误并提高准确性是集成分类中的一个前卫问题。本文提出了一种新颖的通用面向对象投票和加权自适应堆叠框架，用于利用分类器集合进行预测。这个通用框架基于任何一组基础学习者的概率加权平均值运行，最终预测是它们各自投票的总和。出于说明目的，三个熟悉的异构分类器，例如支持向量机，ķ- 最近邻和朴素贝叶斯，被用作使用建议的堆叠框架进行集成分类的候选者。此外，基于该框架构建的集成分类器与其他分类器进行比较，并使用各种交叉验证级别和在一系列基准数据集上的百分比拆分进行评估。结果将框架与竞争区分开来。所提出的框架用于最准确地预测囚犯的犯罪倾向，准确率为 99.9901%。