By Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, Ricardo Vilalta
Metalearning is the research of principled equipment that take advantage of metaknowledge to acquire effective types and strategies via adapting laptop studying and information mining techniques. whereas the range of desktop studying and knowledge mining suggestions now to be had can, in precept, offer stable version ideas, a technique continues to be had to advisor the hunt for the main applicable version in a good means. Metalearning offers one such method that permits platforms to develop into better via experience.
This e-book discusses a number of ways to acquiring wisdom about the functionality of computer studying and knowledge mining algorithms. It indicates how this information may be reused to pick, mix, compose and adapt either algorithms and versions to yield swifter, more suitable ideas to facts mining difficulties. it may hence support builders enhance their algorithms and in addition increase studying platforms which could increase themselves.
The e-book can be of curiosity to researchers and graduate scholars within the parts of computer studying, info mining and synthetic intelligence.
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Extra info for Metalearning: Applications to Data Mining
Example text
Although complete and incomplete rankings can also be named total and partial rankings, we prefer to use the former terminology because total and partial orders are reflexive, which is not the case with the “significantly better than” relation. 2 Meta-level Learning a3 a1 a1 a3 a5 a1 a5 a6 a4 a6 a4 a3 a5 a2 a6 (b) (c) a2 (a) Fig. 2. Representation of rankings using Hasse diagrams: a) linear and complete ranking; b) weak and complete ranking; c) linear and incomplete ranking ranking approach described in the previous chapter that identifies algorithms which are expected to tie, and provides reduced rankings by actually including only one of them in the recommendation.
If this is not the case, the savings obtained by not executing 3 Development of Metalearning Systems for Algorithm Recommendation 43 all the candidate algorithms may not compensate for the cost of computing the measures used to characterize datasets. Pfahringer et al. [190] argued that the computational complexity of metafeatures should be at most O (n log n). Dimensionality. The number of metafeatures should not be too large compared to the amount of available metadata; otherwise overfitting may occur.
A particular case is when the base-algorithms represent the same algorithm with different parameter settings. In the case of selecting parameters for the kernel of SVM, it has been shown that better results are obtained with algorithm-specific metafeatures than with general ones [235]. The metafeatures used in this work were based on the kernel matrices for the different kernel parameters considered. In a different approach to the selection of the kernel parameters for SVM, metafeatures characterizing the kernel matrix were combined with other metafeatures describing the data in terms of its relation to the margin [268].