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LIONsolver

LIONsolver is an integrated software for data mining , business intelligence , analytics , and modeling and reactive business intelligence approach. [ 1 ] A non-profit version i...

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LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling and reactive business intelligence approach.[1] A non-profit version is also available as LIONoso.

LIONsolver is used to build models, visualize them, and improve business and engineering processes.

It is a tool for decision making based on data and quantitative model and it can be connected to most databases and external programs.

The software is fully integrated with the Grapheur business intelligence and intended for more advanced users.

Overview

LIONsolver originates from research principles in Reactive Search Optimization[2] advocating the use of self-tuning schemes acting while a software system is running. Learning and Intelligent OptimizatioN refers to the integration of online machine learning schemes into the optimization software, so that it becomes capable of learning from its previous runs and from human feedback. A related approach is that of Programming by Optimization,[3] which provides a direct way of defining design spaces involving Reactive Search Optimization, and of Autonomous Search [4] advocating adapting problem-solving algorithms.

Version 2.0 of the software was released on Oct 1, 2011, covering also the Unix and Mac OS X operating systems in addition to Windows.

The modeling components include neural networks, polynomials, locally weighted Bayesian regression, k-means clustering, and self-organizing maps. A free academic license for non-commercial use and class use is available.

The software architecture of LIONsolver[5] permits interactive multi-objective optimization, with a user interface for visualizing the results and facilitating the solution analysis and decision-making process. The architecture allows for problem-specific extensions, and it is applicable as a post-processing tool for all optimization schemes with a number of different potential solutions. When the architecture is tightly coupled to a specific problem-solving or optimization method, effective interactive schemes where the final decision maker is in the loop can be developed.[6]

El 24 de abril de 2013, LIONsolver recibió el primer premio del Michael J. Fox Foundation – Kaggle Parkinson's Data Challenge, un concurso que aprovecha "la sabiduría colectiva" para beneficiar a las personas con la enfermedad de Parkinson . [ 7 ]

Véase también

Referencias

  1. ^ Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Búsqueda reactiva y optimización inteligente . Springer Verlag . ISBN 978-0-387-09623-0.
  2. ^ Battiti, Roberto; Gianpietro Tecchiolli (1994). «La búsqueda tabú reactiva» (PDF) . Revista ORSA de Informática . 6 (2): 126– 140. doi : 10.1287/ijoc.6.2.126 .
  3. ↑ Holger, Hoos (2012). "Programación mediante optimización" . Communications of the ACM . 55 (2): 70– 80. doi : 10.1145/2076450.2076469 .
  4. ^ Youssef, Hamadi; E. Monfroy; F. Saubion (2012). Búsqueda Autónoma . Nueva York: Springer Verlag . ISBN 978-3-642-21433-2.
  5. ↑ Battiti, Roberto; Mauro Brunato (2010). Aprendizaje y optimización inteligente [ Actas de Aprendizaje y Optimización Inteligente LION 4, 18-22 de enero de 2010, Venecia, Italia. ] (PDF) . Lecture Notes in Computer Science. Vol. 6073. pp. 232–246 . doi : 10.1007/978-3-642-13800-3 . ISBN   978-3-642-13799-0.
  6. ↑ Battiti, Roberto; Andrea Passerini (2010). "Optimización multiobjetivo evolutiva cerebro-computadora (BC-EMO): un algoritmo genético que se adapta al responsable de la toma de decisiones" (PDF) . IEEE Transactions on Evolutionary Computation . 14 (15): 671– 687. doi : 10.1109/TEVC.2010.2058118 .
  7. ↑ "Un método de aprendizaje automático aplicado a datos de teléfonos inteligentes obtiene el primer premio de 10 000 dólares en el concurso de datos sobre la enfermedad de Parkinson de la Fundación Michael J. Fox . MJFF. 24 de abril de 2013.
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