Swarm Intelligence;Volume 11, Issue 3–4
Ant Colony Optimisation 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 ant colony optimisation 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-leveling. 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+.
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