37,348 research outputs found
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
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Advancing the state of the art in the modelling and simulation of information systems evaluation
It is widely accepted that Information Systems Evaluation (ISE) is a powerful and useful technique
that can be used to assess IT/IS investments in an a-priori or a-posteriori sense. Traditional
approaches to ISE have tended to centre upon financial and management accounting frameworks,
seeking to reconcile tangible and intangible costs, benefits, risks and value factors. Such techniques,
however, do not provide the IS researcher or practitioner with further insight or appreciation of any
inherent and implicit inter-relationships, in the investment justification process. Thus, this paper
outlines and discusses via a taxonomy and resulting classification, alternative and complementary
approaches that can be applied to ISE from the fields of Artificial Intelligence (AI), Operational
Research (OR) and Management Science (MS). The paper subsequently concludes that such
approaches can be potentially used by researchers and practitioners in the field, as a basis for
carrying out further research in the field of applied ISE
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