There are hundreds of algorithms within data mining. Some of them are used to transform
data, some to build classifiers, others for prediction, etc. Nobody knows well all these …

We present a novel approach to meta-learning, which is not just a ranking of methods, not
just a strategy for building model committees, but an algorithm performing a search similar to …

Meta-learning has many aspects, but its final goal is to discover in an automatic way many
interesting models for a given data. Our early attempts in this area involved heterogeneous …

In the early days of pattern recognition and statistical data analysis life was rather simple:
datasets were relatively small, collected from well-designed experiments, analyzed using a …

While a valid intellectual challenge in its own right, meta-learning finds its real raison d'être
in the practical support it offers Data Mining practitioners [20]. Indeed, the whole point of …

There are many data mining systems derived from machine learning, neural network,
statistics and other fields. Most of them are dedicated to some particular algorithms or …

Meta-learning has been accepted, in the last five years, as a proper machine learning
research field. In this concrete area of interest, the way in which different theories, each one …

A data mining algorithm may perform differently on datasets with different characteristics, eg,
it might perform better on a dataset with continuous attributes rather than with categorical …

Current data mining tools are characterized by a plethora of algorithms but a lack of
guidelines to select the right method according to the nature of the problem under analysis …

In data mining, an important early decision for a user to make is to choose an appropriate
technique for analyzing the dataset at hand so that generalizations can be learned …