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A Novel Algorithm Based on Decision Trees in Multiclass Classification

机译：基于决策树的多类分类新算法

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Classification is the most important part in BrainComputer Interface problems, where our task is to decipher the individual's (usually people with physical or verbal disorders) intention from several candidates. In our study, the MEG signals were recorded from an individual when he was shown 5 different types of video clips while our task was to process the MEG signals in each experiment to guess the type of the movie from 5 candidates. In this study, we applied various approaches to this multiclass classification problem and in the end, we proposed a novel algorithm which can also be applied to any multiclass classification problem. Suppose that we are using a decision tree and at each node, the classes are going to be divided into two groups of classes. In the proposed algorithm, we defined a criterion to fmd the best partitioning by using the results of only (n/2) classifications between each pair of classes using training data. As a result, the algorithm is polynomial and can be applied to any multiclass problem. Moreover, as a matter of accuracy, it led us to the best accuracy (61.4%) in comparison to other routine methods. Thus, this algorithm might be a powerful tool in any multiclass classification problem.
机译：分类是BrainComputer界面问题中最重要的部分，其中我们的任务是从多个应聘者中分辨出个人（通常是身体或语言障碍的人）的意图。在我们的研究中，当一个人看到5种不同类型的视频剪辑时，他是从一个人那里录制MEG信号的，而我们的任务是在每个实验中处理MEG信号，以从5个候选对象中猜测电影的类型。在这项研究中，我们对这种多类分类问题应用了各种方法，最后，我们提出了一种新颖的算法，该算法也可以应用于任何多类分类问题。假设我们正在使用决策树，并且在每个节点上，这些类将被划分为两组类。在提出的算法中，我们通过使用训练数据使用每对类别之间仅（n / 2）个分类的结果，定义了一种最佳分割的标准。结果，该算法是多项式的，可以应用于任何多类问题。此外，就准确性而言，与其他常规方法相比，它使我们获得了最高的准确性（61.4％）。因此，该算法可能是解决任何多类分类问题的有力工具。

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