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
M3 - Conference contribution
AN - SCOPUS:32844472774
SN - 1577352343
T3 - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence
SP - 418
EP - 423
BT - Recent Advances in Artifical Intelligence - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
A2 - Russell, I.
A2 - Markov, Z.
T2 - Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
Y2 - 15 May 2005 through 17 May 2005
ER -