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 …

A large number of data mining algorithms exist, rooted in the fields of machine learning,
statistics, pattern recognition, artificial intelligence, and database systems, which are used to …

Classification is one of the primary tasks of data mining and aims to assign a class label to
unseen examples by using a model learned from a training dataset. Most of the accepted …

Selecting a right classification algorithm is an important step for the success of any data
mining project. Run time can be used to assess efficiency of a classification algorithm of …

Abstract Knowledge gained by meta-learning processes is valuable when it can be
successfully used in solving algorithm selection problems. There is still strong need for …

A number of algorithms are available in the areas of data mining, machine learning and
pattern recognition for solving the same kind of problem. But there is a little guidance for …

Given a learning task, the standard approach is to experiment with a broad range of
algorithms and parameter settings, and select the model which performs best according to …

One of the most challenging tasks in the knowledge discovery process is the selection of the
best classification algorithm for a data set at hand. Thus, tools which help practitioners to …

This paper investigates the use of meta-learning to estimate the predictive accuracy of a
classifier. We present a scenario where meta-learning is seen as a regression task and …

The field of Machine Learning has been rapidly gaining attention from both academic and
commercial parties. To promote fast deployement of analytical solutions, several tools have …