7,880 research outputs found
Binary Particle Swarm Optimization based Biclustering of Web usage Data
Web mining is the nontrivial process to discover valid, novel, potentially
useful knowledge from web data using the data mining techniques or methods. It
may give information that is useful for improving the services offered by web
portals and information access and retrieval tools. With the rapid development
of biclustering, more researchers have applied the biclustering technique to
different fields in recent years. When biclustering approach is applied to the
web usage data it automatically captures the hidden browsing patterns from it
in the form of biclusters. In this work, swarm intelligent technique is
combined with biclustering approach to propose an algorithm called Binary
Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The
main objective of this algorithm is to retrieve the global optimal bicluster
from the web usage data. These biclusters contain relationships between web
users and web pages which are useful for the E-Commerce applications like web
advertising and marketing. Experiments are conducted on real dataset to prove
the efficiency of the proposed algorithms
Detecting compact galactic binaries using a hybrid swarm-based algorithm
Compact binaries in our galaxy are expected to be one of the main sources of
gravitational waves for the future eLISA mission. During the mission lifetime,
many thousands of galactic binaries should be individually resolved. However,
the identification of the sources, and the extraction of the signal parameters
in a noisy environment are real challenges for data analysis. So far,
stochastic searches have proven to be the most successful for this problem. In
this work we present the first application of a swarm-based algorithm combining
Particle Swarm Optimization and Differential Evolution. These algorithms have
been shown to converge faster to global solutions on complicated likelihood
surfaces than other stochastic methods. We first demonstrate the effectiveness
of the algorithm for the case of a single binary in a 1 mHz search bandwidth.
This interesting problem gave the algorithm plenty of opportunity to fail, as
it can be easier to find a strong noise peak rather than the signal itself.
After a successful detection of a fictitious low-frequency source, as well as
the verification binary RXJ0806.3+1527, we then applied the algorithm to the
detection of multiple binaries, over different search bandwidths, in the cases
of low and mild source confusion. In all cases, we show that we can
successfully identify the sources, and recover the true parameters within a
99\% credible interval.Comment: 19 pages, 5 figure
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Back-propagation algorithm is one of the most widely used and popular
techniques to optimize the feed forward neural network training. Nature
inspired meta-heuristic algorithms also provide derivative-free solution to
optimize complex problem. Artificial bee colony algorithm is a nature inspired
meta-heuristic algorithm, mimicking the foraging or food source searching
behaviour of bees in a bee colony and this algorithm is implemented in several
applications for an improved optimized outcome. The proposed method in this
paper includes an improved artificial bee colony algorithm based
back-propagation neural network training method for fast and improved
convergence rate of the hybrid neural network learning method. The result is
analysed with the genetic algorithm based back-propagation method, and it is
another hybridized procedure of its kind. Analysis is performed over standard
data sets, reflecting the light of efficiency of proposed method in terms of
convergence speed and rate.Comment: 14 Pages, 11 figure
Building nearest prototype classifiers using a Michigan approach PSO
IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results
Extended two-stage adaptive designswith three target responses forphase II clinical trials
We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization tofind extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial.Our proposed designs include the celebrated Simon’s two-stage design and its extension that allows two target responserates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperformsgreedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capableof solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of ourproposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one ofthe three target response rates of the drug, subject to various user-specified testing error rates. Our designs aretherefore more flexible and interestingly, do not necessarily require larger expected sample size requirements thantwo-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires onehalf fewer subjects than the implemented design in the study
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