Graphics Reference
In-Depth Information
It reduces programming work
. It includes libraries of different paradigms as
evolutionary learning algorithms based on different paradigms (Pittsburgh, Michi-
gan and IRL), fuzzy learning, lazy learning, ANNs, SVMs models and many more;
simplifying the integration of DM algorithms with different pre-processing tech-
niques. It can alleviate the work of programming and enable researchers to focus
on the analysis of their new learning models in comparison with the existing ones.
It extends the range of possible users applying ML algorithms
. An extensive library
of ML techniques together with easy-to-use software considerably reduce the
level of knowledge and experience required by researchers in DM. As a result
researchers with less knowledge, when using this tool, would be able to success-
fully apply these algorithms to their problems.
It has an unparalleled range of preprocessing methods included for DM
, from
discretization algorithms to noisy data filters. Few DM platforms offer the same
amount of preprocessing techniques as KEEL does. This fact combined with a
well-known data format facilitates the user to treat and include their data in the
KEEL work flow and to easily prepare it to be used with their favourite techniques.
Cross platform compatibility
. Due to the use of a strict object-oriented approach
for the library and software tool, these can be used on any machine with Java. As a
result, any researcher can use KEEL on their machine, regardless of the operating
system.
10.2 KEEL: Knowledge Extraction Based on Evolutionary
Learning
KEEL
12
is a software tool that facilitates the analysis of the behaviour of ML in the
different areas of learning and pre-processing tasks, making the management of these
techniques easy for the user. The models correspond with the most well-known and
employedmodels in eachmethodology, such as feature and instance selection [
9
,
10
],
decision trees [
11
], SVMs [
12
], noise filters [
13
], lazy learning [
14
], evolutionary
fuzzy rule learning [
15
], genetic ANNs [
16
], Learning Classifier Systems [
17
], and
many more.
The current available version of KEEL consists of the following function blocks
13
:
Data Management
: This part is made up of a set of tools that can be used to build
new data, to export and import data in other formats to or from KEEL format, data
edition and visualization, to apply transformations and partitioning to data, etc…
Design of Experiments (off-line module)
: The aim of this part is the design of the
desired experimentation over the selected data sets and providing for many options
in different areas: type of validation, type of learning (classification, regression,
unsupervised learning), etc…
