Ant colony optimisation (ACO) for classification has mostly been limited to rule-based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof-of-concept polygon-based classifier that resorts to ACO as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classification of only two classes, including two features per class. This paper introduces PolyACO+, which is an extension of PolyACO in three significant ways: (1) PolyACO+ supports classifying multiple classes, (2) PolyACO+ supports polygons in multiple dimensions enabling classification with more than two features, and (3) PolyACO+ substantially reduces the training time compared to PolyACO by using the concept of multi-levelling. This paper empirically demonstrates that these updates improve the algorithm to such a degree that it becomes comparable to state-of-the-art techniques such as SVM, neural networks, and AntMiner+.

中文翻译：

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PolyACO +：基于多边形的多层次蚁群优化分类器

用于分类的蚁群优化（ACO）主要限于基于规则的方法，在这种方法中，人工蚂蚁在数据集上行走以从数据趋势中提取规则，而混合方法则试图通过引导特征来提高现有分类器的性能。减少或参数优化。与主流方法不同的一个最近的著名例子是PolyACO，它是一种基于概念验证的基于多边形的分类器，它利用ACO作为创建多边缘多边形作为类分隔符的技术。尽管具有一定的前景，PolyACO仍存在一些重大局限性，最显着的事实是仅支持两个类别的分类，每个类别包括两个功能。本文介绍了PolyACO +，它是PolyACO在三个方面的扩展：（1）PolyACO +支持对多个类别进行分类；（2）PolyACO +支持具有多个维度的多边形，从而可以使用两个以上的特征进行分类；（3）与PolyACO相比，PolyACO +通过使用多级别概念大大减少了训练时间。本文凭经验证明，这些更新将算法改进到可以与SVM，神经网络和AntMiner +等最新技术媲美的程度。