将元启发式算法与分类器结合，实现不依赖数据结构的自动数据挖掘。
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining
- 用元启发式优化模型参数，摆脱对特定数据格式的依赖。
- 在MNIST上实验发现，验证集准确率无法有效修正未见数据标签。
- 适合需要跨数据类型通用处理的自动化分析场景。
在机器学习等研究领域，缺乏可实验的数据是常见问题。多数自动化数据挖掘技术难以泛化到所有数据类型，因其依赖特定数据形式，导致对稍有差异的数据表现不佳。元启发式算法能独立于数据类型进行优化，而分类器或神经网络则专注于特征提取与降维以适配特定排列的数据。这两类方法结合后，似乎可在任意数据排列下实现数据挖掘。为此，本文提出将元启发式方法与传统分类器、神经网络相结合，实现自动化数据挖掘。在手写数字识别的MNIST数据集上进行了实验，结果表明：仅使用已标注数据集的验证准确率，不足以修正其他未见过的数据实例的标签。
原文摘要 · Abstract (English)
Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances.
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