### Abstract

For a given classification task, there are typically several learning algorithms available. The question then arises: which is the most appropriate algorithm to apply. Recently, we proposed a new algorithm for making such a selection based on landmarking - a meta-learning strategy that utilises meta-features that are measurements based on efficient learning algorithms. This algorithm, which creates a set of landmarkers that each utilise subsets of the algorithms being landmarked, was shown to be able to estimate accuracy well, even when employing a small fraction of the given algorithms. However, that version of the algorithm has exponential computational complexity for training. In this paper, we propose a hill-climbing version of the landmarker generation algorithm, which requires only polynomial training time complexity. Our experiments show that the landmarkers formed have similar results to the more complex version of the algorithm.

Original language | English |
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Title of host publication | Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence |

Editors | I. Russell, Z. Markov |

Pages | 418-423 |

Number of pages | 6 |

Publication status | Published - 2005 |

Externally published | Yes |

Event | Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Clearwater Beach, FL Duration: 15 May 2005 → 17 May 2005 |

### Other

Other | Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 |
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City | Clearwater Beach, FL |

Period | 15/5/05 → 17/5/05 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence*(pp. 418-423)

**A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria.** / Ler, Daren; Koprinska, Irena; Chawla, Sanjay.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence.*pp. 418-423, Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005, Clearwater Beach, FL, 15/5/05.

}

TY - GEN

T1 - A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria

AU - Ler, Daren

AU - Koprinska, Irena

AU - Chawla, Sanjay

PY - 2005

Y1 - 2005

N2 - For a given classification task, there are typically several learning algorithms available. The question then arises: which is the most appropriate algorithm to apply. Recently, we proposed a new algorithm for making such a selection based on landmarking - a meta-learning strategy that utilises meta-features that are measurements based on efficient learning algorithms. This algorithm, which creates a set of landmarkers that each utilise subsets of the algorithms being landmarked, was shown to be able to estimate accuracy well, even when employing a small fraction of the given algorithms. However, that version of the algorithm has exponential computational complexity for training. In this paper, we propose a hill-climbing version of the landmarker generation algorithm, which requires only polynomial training time complexity. Our experiments show that the landmarkers formed have similar results to the more complex version of the algorithm.

AB - For a given classification task, there are typically several learning algorithms available. The question then arises: which is the most appropriate algorithm to apply. Recently, we proposed a new algorithm for making such a selection based on landmarking - a meta-learning strategy that utilises meta-features that are measurements based on efficient learning algorithms. This algorithm, which creates a set of landmarkers that each utilise subsets of the algorithms being landmarked, was shown to be able to estimate accuracy well, even when employing a small fraction of the given algorithms. However, that version of the algorithm has exponential computational complexity for training. In this paper, we propose a hill-climbing version of the landmarker generation algorithm, which requires only polynomial training time complexity. Our experiments show that the landmarkers formed have similar results to the more complex version of the algorithm.

UR - http://www.scopus.com/inward/record.url?scp=32844472774&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=32844472774&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:32844472774

SN - 1577352343

SP - 418

EP - 423

BT - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence

A2 - Russell, I.

A2 - Markov, Z.

ER -