Graphics Reference
In-Depth Information
It contains a Knowledge Extraction Algorithms Library
15
with the incorporation
of multiple evolutionary learning algorithms, together with classical learning
approaches. The principal families of techniques included are:
-
Evolutionary rule learning models
. Including different paradigms of evolution-
ary learning.
-
Fuzzy systems
. Fuzzy rule learning models with a good trade-off between accu-
racy and interpretability.
-
Evolutionary neural networks
. Evolution and pruning in ANNs, product unit
ANNs, and RBFN models.
-
Genetic programing
. Evolutionary algorithms that use tree representations for
knowledge extraction.
-
Subgroup discovery
. Algorithms for extracting descriptive rules based on pat-
terns subgroup discovery.
-
Data reduction
(
instance and feature selection and discretization
). EAs for data
reduction.
KEEL integrates the library of algorithms in each of its function blocks. We have
briefly presented its function blocks above but in the following subsections, we will
describe the possibilities that KEEL offers in relation to data management, off-line
experiment design and on-line educational design.
10.2.2 Data Management
The fundamental purpose of data preparation is to manipulate and transform raw
data so that the information content enfolded in the data set can be exposed, or made
more accessible [
19
]. Data preparation comprises of those techniques concerned with
analyzing raw data so as to yield quality data, mainly including data collecting, data
integration, data transformation, data cleaning, data reduction and data discretization
[
20
]. Data preparation can be even more time consuming than DM, and can present
similar challenges. Its importance lies in that the real-world data is impure (incom-
plete, noisy and inconsistent) and high-performance mining systems require quality
data (the removal of anomalies or duplications). Quality data yields high-quality
patterns (to recover missing data, purify data and resolve conflicts).
The
Data Management
module integrated in KEEL allows us to perform the data
preparation stage independently of the remaining DM processes. This module is
focused on the group of users denoted as domain experts. They are familiar with
their data, they know the processes that produce the data and they are interested in
reviewing to improve them or analyze them. On the other hand, domain users are
those whose interests lies in applying processes to their own data and are usually not
experts in DM.
