5,071 research outputs found
An Interactive Approach Based on Alternative Achievement Scale and Alternative Comprehensive Scale for Multiple Attribute Decision Making under Linguistic Environment
The aim of this paper is to develop an interactive approach for multiple attribute decision making with incomplete
weight information under linguistic environment. Some of the concepts are defined, such as the distance between
two 2-tuple linguistic variables, the expectation level of alternative, the achievement scale, the alternative
comprehensive scale under linguistic environment. Based on these concepts, we establish some linear programming
models, through which the decision maker interacts with the analyst. Furthermore, we establish a practical
interactive approach for selecting the most desirable alternative(s). The interactive process can be realized by
giving and revising the achievement scale and comprehensive scale of alternatives till the achievement scale and
the comprehensive scale are achieved to the decision maker’s request. Finally, an illustrative example is also given.The author is very grateful to the associated editor and two anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. This work was partly supported by the National Natural Science Foundation of China (No. 90924027, No. 71101043), National Basic Research Program of China (973 Program, No. 2010C B951104), Key Program of National Social Science Foundation of China (No. 10AJY005), College Philosophy and Social Science Research Project of Jiangsu Province under Grant 2011SJD630007.Xu, Y.; Wang, H.; Palacios Marqués, D. (2013). An Interactive Approach Based on Alternative Achievement Scale and Alternative Comprehensive Scale for Multiple Attribute Decision Making under Linguistic Environment. International Journal of Computational Intelligence Systems. 6(1):87-95. https://doi.org/10.1080/18756891.2013.756226S87956
Towards a multimedia knowledge-based agent with social competence and human interaction capabilities
We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.The presented work is funded by the European Commission under the contract number H2020-645012-RIA
Big data and the SP theory of intelligence
This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as "a universal framework for the representation and
processing of diverse kinds of knowledge" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.Comment: Accepted for publication in IEEE Acces
Text Classification: A Review, Empirical, and Experimental Evaluation
The explosive and widespread growth of data necessitates the use of text
classification to extract crucial information from vast amounts of data.
Consequently, there has been a surge of research in both classical and deep
learning text classification methods. Despite the numerous methods proposed in
the literature, there is still a pressing need for a comprehensive and
up-to-date survey. Existing survey papers categorize algorithms for text
classification into broad classes, which can lead to the misclassification of
unrelated algorithms and incorrect assessments of their qualities and behaviors
using the same metrics. To address these limitations, our paper introduces a
novel methodological taxonomy that classifies algorithms hierarchically into
fine-grained classes and specific techniques. The taxonomy includes methodology
categories, methodology techniques, and methodology sub-techniques. Our study
is the first survey to utilize this methodological taxonomy for classifying
algorithms for text classification. Furthermore, our study also conducts
empirical evaluation and experimental comparisons and rankings of different
algorithms that employ the same specific sub-technique, different
sub-techniques within the same technique, different techniques within the same
category, and categorie
Granular computing and optimization model-based method for large-scale group decision-making and its application
In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare
some alternatives, so they often express evaluation information
with incomplete hesitant fuzzy linguistic preference relations.
How to obtain suitable large-scale group decision-making results
from incomplete preference information is an important and
interesting issue to concern about. After analyzing the existing
researches, we find that: i) the premise that complete preference
relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given
by decision makers are greatly possible to be changed during the
consistency improving process. In order to solve these issues, this
work proposes a novel method based on Granular computing
and optimization model for large-scale group decision-making,
considering the original consistency of incomplete hesitant fuzzy
linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the
rationality and advantages of the proposed method: i) semantics
are not changed during the consistency improving process, ii)
completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally
consistent, iv) final large-scale group decision-making result is
acquired by fusing complete preference relations with different weights
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
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