publication . Article . 2017

Instance Selection for Classifier Performance Estimation in Meta Learning

Marcin Blachnik;
Open Access English
  • Published: 01 Nov 2017 Journal: Entropy (issn: 1099-4300, Copyright policy)
  • Publisher: MDPI AG
Abstract
Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression l...
Subjects
free text keywords: machine learning, classification, instance selection, meta-learning, accuracy estimation, Science, Q, Astrophysics, QB460-466, Physics, QC1-999, General Physics and Astronomy, Classifier (linguistics), Predictive modelling, Metadata, k-nearest neighbors algorithm, Support vector machine, computer.software_genre, computer, Instance-based learning, Random forest, Data mining, Model selection, Artificial intelligence, business.industry, business, Computer science
Download fromView all 2 versions
Entropy
Article . 2017
Entropy
Article . 2017
Provider: Crossref
Entropy
Article
Provider: UnpayWall
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue