.png){kind=icon}
返回
Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2022.109969.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Meta-learning, a concept from the area of automated machine learning, aims at providing decision support for data scientists by recommending a suitable setting (a machine learning algorithm or its hyper-parameters) to be used for a given dataset. Such a recommendation is based the assumption that an optimal setting for a certain dataset would also be suitable for other, similar datasets. Similarity of datasets is computed from their characteristics, named meta-features, several types of which have been developed thus far. This paper introduces a novel perspective on PCA meta-features which, despite their good descriptive characteristics and easy computation, are rarely used in meta-learning. A novel meta-learning approach utilizing DTW, a well-known similarity measure for time-series, is proposed for computing dataset similarities based on the series of cumulative variances explained by their respective principal components. The results from a large-scale experiment, comparing the proposed approach to multiple baselines on 50 real-world datasets, show the potential of combining PCA and DTW in meta-learning and encourage further investigation in this direction.
Keyword:
Meta-learning
Meta-features
Principal component analysis
Dynamic time warping
Hyper-parameter initialization
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏分享收藏MetaStream: A meta-learning based method for periodic algorithm selection in time-changing dataMetaStream: 一种基于元学习的时变数据周期性算法选择方法.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A meta-learning method to select the kernel width in Support Vector Regression支持向量回归中核宽度选择的元学习方法MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏
分享收藏
查看更多内容