Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Phonocardiography is important in appraisal of
congenital heart disease and pulmonary hypertension as it reflects the
duration of right ventricular systoles. The systolic murmur in patients
with intra-cardiac shunt decreases as pulmonary hypertension
develops and may eventually disappear completely as the pulmonary
pressure reaches systemic level. Phonocardiography and auscultation
are non-invasive, low-cost, and accurate methods to assess heart
disease. In this work an objective signal processing tool to extract
information from phonocardiography signal using Wavelet is
proposed to classify the murmur as normal or abnormal. Since the
feature vector is large, a Binary Particle Swarm Optimization (PSO)
with mutation for feature selection is proposed. The extracted
features improve the classification accuracy and were tested across
various classifiers including Naïve Bayes, kNN, C4.5, and SVM.
Abstract: For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.
Abstract: A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.