Evolutionary computation for the automated design of category functions for fuzzy ART: an initial exploration
Evolutionary computation for the automated design of category functions for fuzzy ART: an initial exploration
复制标题
模糊 ART 类别函数自动设计的进化计算：初步探索
DOI：
10.1145/3067695.3082056
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
D. Wunsch
中科院分区：
文献类型：
--
作者：
Islam El;D. Tauritz;D. Wunsch
模糊自适应共振理论（ART）是一种经典的无监督学习算法。它在特定聚类问题上的表现对所述问题的类别功能的适用性敏感。但是，经典模糊艺术具有固定的类别功能，因此无法从调整其类别功能的潜力中受益。本文对使用进化计算进行类别功能的自动设计进行了探索，以通过量身定制特定问题类别来获得大大增强的模糊艺术表现。我们采用了一种遗传编程的超级先进方法，其中从构成原始模糊艺术类别功能以及其他手动选择的原始图的一组原始函数中构建了类别函数。提出了从UCI机器学习存储库中进行基准分类任务的一组实验的结果，表明裁缝模糊艺术的类别功能可以在分层的10倍交叉验证过程中的测试数据集上实现统计学上的出色性能。我们以讨论结果并将其放置为自动化全新艺术形式的设计的第一步。
Fuzzy Adaptive Resonance Theory (ART) is a classic unsupervised learning algorithm. Its performance on a particular clustering problem is sensitive to the suitability of the category function for said problem. However, classic Fuzzy ART employs a fixed category function and thus is unable to benefit from the potential to adjust its category function. This paper presents an exploration into employing evolutionary computation for the automated design of category functions to obtain significantly enhanced Fuzzy ART performance through tailoring to specific problem classes. We employ a genetic programming powered hyper-heuristic approach where the category functions are constructed from a set of primitives constituting those of the original Fuzzy ART category function as well as additional hand-selected primitives. Results are presented for a set of experiments on benchmark classification tasks from the UCI Machine Learning Repository demonstrating that tailoring Fuzzy ART's category function can achieve statistically significant superior performance on the testing datasets in stratified 10-fold cross-validation procedures. We conclude with discussing the results and placing them in the context of being a first step towards automating the design of entirely new forms of ART.
影响因子：
3.6
作者：
Burke, Edmund K.;Gendreau, Michel;Qu, Rong
通讯作者：
Qu, Rong