Graphics Reference
In-Depth Information
Educational Experiments (on-line module)
: With a similar structure to the
aforementioned, this permits the design of experiment to be run step-by-step in
order to display the learning process of a certain model by using the software tool
for educational purposes.
With all of these function blocks, we can attest that KEEL can be useful by
different types of users who may expect to find specific features in a DM software.
In the following subsections we describe in detail the user profiles for whom
KEEL is intended, its main features and the different integrated function blocks.
10.2.1 Main Features
KEEL is a software tool developed to ensemble and use different DM models.
Although it was initially focused on the use of evolutionary algorithms for KDD,
its continuous development has broadened the available ML paradigms for DM. We
would like to note that this is the first software toolkit of this type containing a
library of evolutionary algorithms with open source code in Java. The main features
of KEEL are:
Almost one hundred of data preprocessing algorithms proposed in specialized
literature are included: data transformation, discretization, MVs treatment, noise
filtering, instance selection and FS.
More than two hundred of state-of-the-art techniques for classification, regression,
subgroup discovery, clustering and association rules, ready to be used within the
platform or to be extracted and integrated in any other particular project.
Specialized modules for recent and difficult challenges in DM such as imbalanced
learning and multiple instance learning.
Being the initial key role of KEEL, EAs are presented in predicting models, pre-
processing (evolutionary feature and instance selection) and post-processing (evo-
lutionary tuning of fuzzy rules).
It contains a statistical library to analyze algorithm results and comprises of a set of
statistical tests for analyzing the normality and heteroscedasticity of the results, as
well as performing parametric and non-parametric comparisons of the algorithms.
Some algorithms have been developed using the Java Class Library for Evolution-
ary Computation (JCLEC) software [
18
].
14
A user-friendly interface is provided, oriented towards the analysis of algorithms.
The software is designed for experiments containing multiple data sets and algo-
rithms connected to each other to obtain the desired result. Experiments are inde-
pendently script-generated from the user interface for an off-line run in the same
or other machines.
KEEL also allows for experiments in on-line mode, intended as an educational
support for learning the operation of the algorithms included.
