3,039 research outputs found
Adaptive imputation of missing values for incomplete pattern classification
In classification of incomplete pattern, the missing values can either play a
crucial role in the class determination, or have only little influence (or
eventually none) on the classification results according to the context. We
propose a credal classification method for incomplete pattern with adaptive
imputation of missing values based on belief function theory. At first, we try
to classify the object (incomplete pattern) based only on the available
attribute values. As underlying principle, we assume that the missing
information is not crucial for the classification if a specific class for the
object can be found using only the available information. In this case, the
object is committed to this particular class. However, if the object cannot be
classified without ambiguity, it means that the missing values play a main role
for achieving an accurate classification. In this case, the missing values will
be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM)
techniques, and the edited pattern with the imputation is then classified. The
(original or edited) pattern is respectively classified according to each
training class, and the classification results represented by basic belief
assignments are fused with proper combination rules for making the credal
classification. The object is allowed to belong with different masses of belief
to the specific classes and meta-classes (which are particular disjunctions of
several single classes). The credal classification captures well the
uncertainty and imprecision of classification, and reduces effectively the rate
of misclassifications thanks to the introduction of meta-classes. The
effectiveness of the proposed method with respect to other classical methods is
demonstrated based on several experiments using artificial and real data sets
What May Visualization Processes Optimize?
In this paper, we present an abstract model of visualization and inference
processes and describe an information-theoretic measure for optimizing such
processes. In order to obtain such an abstraction, we first examined six
classes of workflows in data analysis and visualization, and identified four
levels of typical visualization components, namely disseminative,
observational, analytical and model-developmental visualization. We noticed a
common phenomenon at different levels of visualization, that is, the
transformation of data spaces (referred to as alphabets) usually corresponds to
the reduction of maximal entropy along a workflow. Based on this observation,
we establish an information-theoretic measure of cost-benefit ratio that may be
used as a cost function for optimizing a data visualization process. To
demonstrate the validity of this measure, we examined a number of successful
visualization processes in the literature, and showed that the
information-theoretic measure can mathematically explain the advantages of such
processes over possible alternatives.Comment: 10 page
Distributed Random Set Theoretic Soft/Hard Data Fusion
Research on multisensor data fusion aims at providing the enabling technology to combine
information from several sources in order to form a unifi ed picture. The literature
work on fusion of conventional data provided by non-human (hard) sensors is vast and
well-established. In comparison to conventional fusion systems where input data are generated
by calibrated electronic sensor systems with well-defi ned characteristics, research
on soft data fusion considers combining human-based data expressed preferably in unconstrained
natural language form. Fusion of soft and hard data is even more challenging, yet
necessary in some applications, and has received little attention in the past. Due to being
a rather new area of research, soft/hard data fusion is still in a
edging stage with even
its challenging problems yet to be adequately de fined and explored.
This dissertation develops a framework to enable fusion of both soft and hard data
with the Random Set (RS) theory as the underlying mathematical foundation. Random
set theory is an emerging theory within the data fusion community that, due to its powerful
representational and computational capabilities, is gaining more and more attention among
the data fusion researchers. Motivated by the unique characteristics of the random set
theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying
framework capable of processing both unconventional soft data and conventional hard data,
this dissertation argues in favor of a random set theoretic approach as the first step towards
realizing a soft/hard data fusion framework.
Several challenging problems related to soft/hard fusion systems are addressed in the
proposed framework. First, an extension of the well-known Kalman lter within random
set theory, called Kalman evidential filter (KEF), is adopted as a common data processing
framework for both soft and hard data. Second, a novel ontology (syntax+semantics)
is developed to allow for modeling soft (human-generated) data assuming target tracking
as the application. Third, as soft/hard data fusion is mostly aimed at large networks of
information processing, a new approach is proposed to enable distributed estimation of
soft, as well as hard data, addressing the scalability requirement of such fusion systems.
Fourth, a method for modeling trust in the human agents is developed, which enables the
fusion system to protect itself from erroneous/misleading soft data through discounting
such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data
fusion literature a novel soft data association algorithm is developed and deployed to extend
the proposed target tracking framework into multi-target tracking case. Finally, the
multi-target tracking framework is complemented by introducing a distributed classi fication
approach applicable to target classes described with soft human-generated data.
In addition, this dissertation presents a novel data-centric taxonomy of data fusion
methodologies. In particular, several categories of fusion algorithms have been identifi ed
and discussed based on the data-related challenging aspect(s) addressed. It is intended to
provide the reader with a generic and comprehensive view of the contemporary data fusion
literature, which could also serve as a reference for data fusion practitioners by providing
them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c
data-related challenges expected in a given application
Interval Neutrosophic Sets and Logic: Theory and Applications in Computing
A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study
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