Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]

Autor:	Beel, Joeran, Tyrell, Bryan, Bergman, Edward, Collins, Andrew, Nagoor, Shahad
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features. Our work includes a novel performance metric and method for selecting training samples. We introduce further the concept of 'Algorithm Performance Personas' that describe instances for which the single algorithms perform alike. The concept of 'alike performing algorithms' as ground truth for selecting training samples is novel and provides a huge potential as we believe. In this proposal, we outline our ideas in detail and provide the first evidence that our proposed metric is better suitable for training sample selection that standard performance metrics such as absolute errors. Comment: 7th Workshop on Automated Machine Learning (AutoML 2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.12328 Zobrazit plný text záznamu View this record from Arxiv