6 research outputs found
Determine OWA operator weights using kernel density estimation
Some subjective methods should divide input values into local
clusters before determining the ordered weighted averaging
(OWA) operator weights based on the data distribution characteristics
of input values. However, the process of clustering input values
is complex. In this paper, a novel probability density based
OWA (PDOWA) operator is put forward based on the data distribution
characteristics of input values. To capture the local cluster
structures of input values, the kernel density estimation (KDE) is
used to estimate the probability density function (PDF), which fits
to the input values. The derived PDF contains the density information
of input values, which reflects the importance of input
values. Therefore, the input values with high probability densities
(PDs) should be assigned with large weights, while the ones with
low PDs should be assigned with small weights. Afterwards, the
desirable properties of the proposed PDOWA operator are investigated.
Finally, the proposed PDOWA operator is applied to handle
the multicriteria decision making problem concerning the evaluation
of smart phones and it is compared with some existing
OWA operators. The comparative analysis shows that the proposed
PDOWA operator is simpler and more efficient than the
existing OWA operator
Forecasting: theory and practice
Forecasting has always been in the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The lack of a free-lunch theorem implies the need for a diverse set
of forecasting methods to tackle an array of applications. This unique article
provides a non-systematic review of the theory and the practice of forecasting.
We offer a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts, including operations, economics, finance,
energy, environment, and social good. We do not claim that this review is an
exhaustive list of methods and applications. The list was compiled based on the
expertise and interests of the authors. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has been
undertaken over the last decades, with some key insights for the future of the
forecasting theory and practice
Pattern Recognition
Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition