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Meta-Learning with Landmarking: A Survey

by Ashvini Balte, Nitin Pise, Parag Kulkarni
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: Ashvini Balte, Nitin Pise, Parag Kulkarni
10.5120/18401-9666

Ashvini Balte, Nitin Pise, Parag Kulkarni . Meta-Learning with Landmarking: A Survey. International Journal of Computer Applications. 105, 8 ( November 2014), 47-51. DOI=10.5120/18401-9666

@article{ 10.5120/18401-9666,
author = { Ashvini Balte, Nitin Pise, Parag Kulkarni },
title = { Meta-Learning with Landmarking: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18401-9666/ },
doi = { 10.5120/18401-9666 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:13.367287+05:30
%A Ashvini Balte
%A Nitin Pise
%A Parag Kulkarni
%T Meta-Learning with Landmarking: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 47-51
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Everyday large amount of data is generated. Data mining is a technique that extracts significant information from raw data using different classifiers. Meta-learning is machine learning technique that supports data mining for choosing appropriate classifier. Instead of whole data set, meta-learning extracts features from meta-data. So, it is important to identify the best suitable classifier based on meta-features. Landmarking is uncommon meta-feature extraction technique present in meta-learning. Instead of statistical measurement landmarking tries to determine position of training data in the areas of problem learning by directly measuring the performance of some simple and significant learning algorithms themselves. This paper is a survey of presented approach in landmarking area. A few research challenges are mentioned at the end of this paper.

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Index Terms

Computer Science
Information Sciences

Keywords

Meta-learning Landmarking Algorithm selection Classifier Accuracy

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