| |
 Programme
Overview
Detailed
Programme
| Machine Learning
and Heuristics I (MLHI) |
CHAIR: UROS LOTRIC
Time: Monday 21st March, 11h00-12h40
| MLHI-1 |
| |
| Title: |
|
|
Neural-Networks for Extraction
of Fuzzy Logic Rules with Application to EEG Data |
| Author(s): |
Martin Holena |
| Abstract: |
The extraction of logical rules from data is
a key application of artificial neural networks (ANNs) in
data mining. However, most of the ANN-based rule extraction
methods rely primarily on heuristics, and their underlying
theoretical principles are not very deep. That is especially
much true for methods extracting fuzzy logic rules, which
usually allow to mix different logical connectives in such
a way that extracted rules can not be correctly evaluated
in any particular fuzzy logic model. This paper shows that
mixing of connectives is not needed. A method for fuzzy rules
extraction for which the evaluation of the extracted rules
in a single model is the basic principle is outlined and illustrated
on a case study with EEG data. |
 |
| MLHI-2 |
| |
| Title: |
|
|
A New Neuro-Based Method For Short
Term Load Forecasting of Iran National Power System |
| Author(s): |
R. Barzamini,
M. B. Menhaj,
Sh. Kamalvand,
M.A.Fasihi |
| Abstract: |
This paper presents a new neuro-based method
for short term load forecasting of Iran national power system
(INPS). A MultiLayer Perceptron (MLP) based Neural Network
(NN) toolbox has been develeped to forecast 168 hours ahead.
The proposed MLP has one hiden layer with 5 neurons. The effective
inputs were selected through a peer investigation on historical
data released from the INPS. To adjust the parameters of the
MLP, the Levenberg-Marquardt Back Propagation (LMBP) training
algorithm has been employed because of its remarkable fast
speed of convergence. Most of papers dealt with 168-hour forecasting
employed a hirachical method in the sense of monthly or seasonly
provided that there are enough data. In the absence of rich
data, forecasting error would increase. To remedy this problem,
the proposed neuro-based approach uses only the weekly group
data of concern while an extra input is added up to indicate
the month. In other words for each weekly group, a unique
MLP based neural network is designed for the purposed of load
forecasting. |
|
| MLHI-3 |
| |
| Title: |
|
|
Approximate the Algebraic Solution
of Systems of Interval Linear Equations with Use of Neural
Networks |
| Author(s): |
N. Viet,
M. Kleiber |
| Abstract: |
A new approach to approximate the algebraic
solution of systems of interval linear equations (SILE) is
proposed in this paper. The original SILE problem is first
transformed into an optimization problem, which is in turn
solved with use of artificial neural networks and gradient-based
optimization techniques. |
|
| MLHI-4 |
| |
| Title: |
|
|
Comparing Diversity and Training
Accuracy in Classifier Selection for Plurality Voting Based
Fusion |
| Author(s): |
Hakan Altincay |
| Abstract: |
Selection of an optimal subset of classifiers
in designing classifier ensembles is an important problem.
The search algorithms used for this purpose maximize an objective
function which may be the combined training accuracy or diversity
of the selected classifiers. Taking into account the fact
that there is no benefit in using multiple copies of the same
classifier, it is generally argued that the classifiers should
be diverse and several measures of diversity are proposed
for this purpose. In this paper, the relative strengths of
combined training accuracy and diversity based approaches
are investigated for the plurality voting based combination
rule. Moreover, we propose a diversity measure where the difference
in classification behavior exploited by the plurality voting
combination rule is taken into account. |
|