Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules.

Main features of the mapping study performed in ensemble classification methods applied on lung cancer decision support systems

在研究人员中提供高水平的分类准确性是研究人员提供有效决策系统以协助医生工作的基本需求。在人工智能的许多领域中，集成分类方法能够提高单个分类器的性能。本文报道了肺癌检测中集成分类方法的最新技术。我们已经进行了系统的制图研究，以找出与此主题相关的最有趣的论文。在四个数字图书馆中进行自动搜索并经过仔细选择之后，总共选择了2000年至2018年之间发表的65篇论文。结果，发现诊断是最常研究的任务。均匀合奏和决策树最常用于构建合奏；多数表决规则是主要的合并规则。很少有研究考虑所使用技术的参数调整。这些发现为研究人员通过解决已发现的差距（例如研究不同的分类方法，提出其他异类集成方法以及使用新的组合规则）增强肺癌研究开辟了一些前景。

整体分类方法在肺癌决策支持系统中应用的作图研究的主要特征