85,847 research outputs found
Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Neural networks are found to be attractive trainable machines for pattern recognition.
The capability of these models to accommodate wide variety and variability of
conditions, and the ability to imitate brain functions, make them popular research
area.
This research focuses on developing hybrid rough neural networks. These novel
approaches are assumed to provide superior performance with respect to detection
and automatic target recognition.In this thesis, hybrid architectures of rough set theory and neural networks have been
investigated, developed, and implemented. The first hybrid approach provides novel
neural network referred to as Rough Shared weight Neural Networks (RSNN). It uses
the concept of approximation based on rough neurons to feature extraction, and
experiences the methodology of weight sharing. The network stages are a feature
extraction network, and a classification network. The extraction network is
composed of rough neurons that accounts for the upper and lower approximations
and embeds a membership function to replace ordinary activation functions. The
neural network learns the rough set’s upper and lower approximations as feature
extractors simultaneously with classification. The RSNN implements a novel
approximation transform. The basic design for the network is provided together with
the learning rules. The architecture provides a novel method to pattern recognition
and is expected to be robust to any pattern recognition problem.
The second hybrid approach is a two stand alone subsystems, referred to as Rough
Neural Networks (RNN). The extraction network extracts detectors that represent
pattern’s classes to be supplied to the classification network. It works as a filter for
original distilled features based on equivalence relations and rough set reduction,
while the second is responsible for classification of the outputs from the first system.
The two approaches were applied to image pattern recognition problems. The RSNN
was applied to automatic target recognition problem. The data is Synthetic Aperture
Radar (SAR) image scenes of tanks, and background. The RSNN provides a novel
methodology for designing nonlinear filters without prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite image. A novel
feature extraction algorithm was developed to extract the feature vectors. The
algorithm enhances the recognition ability of the system compared to manual
extraction and labeling of pattern classes. The performance of the rough
backpropagation network is improved compared to backpropagation of the same
architecture. The network has been designed to produce detection plane for the
desired pattern.
The hybrid approaches developed in this thesis provide novel techniques to
recognition static and dynamic representation of patterns. In both domains the rough
set theory improved generalization of the neural networks paradigms. The
methodologies are theoretically robust to any pattern recognition problem, and are
proved practically for image environments
Class Association Rules Mining based Rough Set Method
This paper investigates the mining of class association rules with rough set
approach. In data mining, an association occurs between two set of elements
when one element set happen together with another. A class association rule set
(CARs) is a subset of association rules with classes specified as their
consequences. We present an efficient algorithm for mining the finest class
rule set inspired form Apriori algorithm, where the support and confidence are
computed based on the elementary set of lower approximation included in the
property of rough set theory. Our proposed approach has been shown very
effective, where the rough set approach for class association discovery is much
simpler than the classic association method.Comment: 10 pages, 2 figure
Aggregation procedure based on majority principle for collective identification of firm’s crucial knowledge
It is very important to identify, preserve, and transfer knowledge to those who need it within firm. However, the identification of knowledge and especially tacit knowledge is a complex process because knowledge cannot be measured quantitatively. In this paper we present an approach for inducing a set of collective decision rules representing a generalized description of the preferential information of a group of decision makers involved in a multicriteria classification problem to identify crucial knowledge to be capitalized
Domains via approximation operators
In this paper, we tailor-make new approximation operators inspired by rough
set theory and specially suited for domain theory. Our approximation operators
offer a fresh perspective to existing concepts and results in domain theory,
but also reveal ways to establishing novel domain-theoretic results. For
instance, (1) the well-known interpolation property of the way-below relation
on a continuous poset is equivalent to the idempotence of a certain
set-operator; (2) the continuity of a poset can be characterized by the
coincidence of the Scott closure operator and the upper approximation operator
induced by the way below relation; (3) meet-continuity can be established from
a certain property of the topological closure operator. Additionally, we show
how, to each approximating relation, an associated order-compatible topology
can be defined in such a way that for the case of a continuous poset the
topology associated to the way-below relation is exactly the Scott topology. A
preliminary investigation is carried out on this new topology.Comment: 17 pages; 1figure, Domains XII Worksho
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