High Order Computational Intelligence in Data Mining A generic approach to systemic intelligent Data Mining

Neukart, Florian; Grigorescu, Costin-Marius; Moraru, Sorin-Aurel

doi:10.1109/sped.2011.5940746

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The application of computational intelligence techniques has had a decisive impact in other fields such as data mining [32]. Figure 4 shows the four most-used data mining tools worldwide according to a survey by KDnuggets in 2015 to 2759 voters b .…”

Section: Computational Intelligence and It Toolsmentioning

confidence: 99%

Project Control and Computational Intelligence: Trends and Challenges

Garcia¹,

Peña²,

Pérez³

et al. 2017

IJCIS

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Project monitoring and control by using key performance indicators has become a widespread method for decisionmaking in project-oriented organizations. However, the current schools and IT tools created for this purpose require an upgrade in design due to imprecision, vagueness or uncertainty present in the raw data and changing conditions in management styles. Moreover, the use of proprietary technologies in developing nations represents high costs for governments and obstacles to achieving its technological sovereignty. This paper studies the trends and challenges in project control through computational intelligence methods. It also examines schools and technological tools to manage projects, as well as open source software for the application of computational intelligence techniques over the past decades. Current tendencies and improvement areas, valuing niche markets with high applicability around the thematic goal it is also analyzed. The contribution of this study is related to the predicted necessity of constructing new models and IT tools for project control which integrate machine learning-based approaches and treatment of imprecision, vagueness or uncertainty in the information, using key performance indicators linked to fundamental knowledge areas. The implementation of new libraries for learning evaluation in project control with open source software tools, opens a field of research related to increase technological integration with IT project management tools. The content under discussion provides support to improve decision-making in project-oriented organizations.

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Section: Computational Intelligence and It Toolsmentioning

confidence: 99%

Project Control and Computational Intelligence: Trends and Challenges

Garcia¹,

Peña²,

Pérez³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…The summarization of the single agent's skills empowers them to solve complex Data Mining problems. Future research will target BDI-agents for possibly increasing the further increase of the system's performance (Neukart et al, 2011).…”

Section: Future Researchmentioning

confidence: 99%

“…

The already introduced generic Data Mining system SHOCID (System applying High Order Computational Intelligence in Data Mining) (Neukart et al, 2011) applies Computational Intelligence (CI) paradigms for solving any numeric Data Mining problem statement. Within this paper, we introduce the evolutionary approach by which the system is able to decide on its own, which of the possible evolutionary approaches suits best for solving a presented problem statement.

…”

mentioning

confidence: 99%

Problem-Dependent, Genetically Evolving Data Mining Solutions

Neukart¹,

Moraru²,

Simon³

2011

DAAAM Proceedings

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The already introduced generic Data Mining system SHOCID (System applying High Order Computational Intelligence in Data Mining) (Neukart et al., 2011) applies Computational Intelligence (CI) paradigms for solving any numeric Data Mining problem statement. Within this paper, we introduce the evolutionary approach by which the system is able to decide on its own, which of the possible evolutionary approaches suits best for solving a presented problem statement. Moreover, the system is, by the application of genetic algorithms, able to adapt the architecture and learning method of the Data Mining solution until coming to or at least close to the optimal solution.

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“…Since these processes try to protect e.g. humans from biologically and biochemically hazardous entities, it has been reasoned that they may be computationally useful [1].…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System-Inspired Neuroevolution

Neukart¹,

Moraru²,

Simon³

2012

Proceedings of the 23rd International DAAAM Symposium 2012

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Artificial ImmuneSystem (AIS)-inspired NeuroEvolution combines the advantages genetic algorithms feature with abstractions of immunological processes. Such processes, applied by immune systems trying to protect organisms from biologically and biochemically hazardous entities, intensely increase the learning performance and accuracy of Multi-Layer Perceptrons performing a stochastic search in a space. This is achieved by applying a combination of immunological operations in each population's evolution cycle, which are clonal selection and somatic hypermutation, negative selection and danger theory. Furthermore, causality plays an important role within the introduced paradigm, as the solution population does not only change from generation to generation, but also within each generation. For the immune system-based operations only the individuals of the current generation do matter. Thus, the in-and outputs a population in consideration processes when learning admittedly have a significance over time for the genetic evolution of a single individual (chromosome). However, all of the immune systembased operations do not need to consider these, as only the current population of genomes matters. Currently, only the already introduced, computationally intelligent Data Mining system "System applying High Order Computational Intelligence in Data Mining" (SHOCID) successfully applies the introduced approach for Artificial Neural Network learning.

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High Order Computational Intelligence in Data Mining A generic approach to systemic intelligent Data Mining

Cited by 7 publications

References 5 publications

Project Control and Computational Intelligence: Trends and Challenges

Project Control and Computational Intelligence: Trends and Challenges

Problem-Dependent, Genetically Evolving Data Mining Solutions

Artificial Immune System-Inspired Neuroevolution

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