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Fuzzy Systems and Soft Computing

We study various computational methods which are mainly applied in data analysis. Our practical aim is to produce new methods and tools from fuzzy set theory to be applied in the field of data analysis. We focus especially on the following generic topics:

* Classification methods based on fuzzy set theory and evolutionary computing

* Feature selection and feature extraction method based on fuzzy measures

* Fuzzy principal component analysis methods

* Fuzzy multi-expert multicriteria decision making

* Fuzzy linear systems

Short introduction couple of the project we have going on:

Fuzzy linear systems

We have created new models and applied them to i.e. Leontief input-output model with emphasis on pulp and paper sector. Resent publications:

* M Hujala, P Luukka, H Arminen, K Puumalainen, JK Mattila, Pulp and paper industry in traditional and new markets–a fuzzy input-output analysis, 2014, International Journal of Procurement Management 7 (6), pp. 639-660

  • Data sets applied in this paper: Data sets (in excel format).

* C Panchal, P Luukka, JK Mattila, Leontief input-output model with trapezoidal fuzzy numbers and Gauss-Seidel algorithm, 2014, International Journal of Process Management and Benchmarking 4 (4), pp. 456-474

Differential evolution based classification

In this research group people currently involved are: David Koloseni and Pasi Luukka from LUT and Jouni Lampinen from Vaasa University. Main aim in this research area is to further develop differential evolution based classification methods. Besides this we also concentrate on how differential evolution algorithm can be applied to other problems in data analysis i.e. in feature selection. Also classification has shown to be an ideal testing ground to differential evolution algorithms.

We have collaboration with following partners:

  • Vaasa University, Finland
  • VSB-Technical University of Ostrava, Ostrava, Czech Republic

Recent publications:

* Koloseni, D, Lampinen, J. Luukka, P., Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data set, Expert Systems with Applications, 40 (2013), pp. 4075-4082.

* Koloseni, D, Lampinen, J. Luukka, P., Optimized distance metrics for differential evolution based nearest prototype classifier, Expert Systems with Applications, 39 (2012), pp. 10564-10570. Link to classifiers Matlab files.

*David Koloseni, Jouni Lampinen, Pasi Luukka: Differential evolution classifier with optimized distance measures from a pool of distances. IEEE Congress on Evolutionary Computation 2012: 1-7

*David Koloseni, Jouni Lampinen, Pasi Luukka: Differential Evolution Classifier with Optimized Distance Measures for the Features in the Data Sets. SOCO 2012: 103-111

Fuzzy multiple criteria decision making

In this research group people currently involved are: Pasi Luukka, Mikael Collan and Leoncie Niyigena from LUT. We concentrate on developing new methods in decision making. Emphasis is given on multiple expert, multiple criteria decision making problems.

We have collaboration with following partners:

  • Trento University, Italy
  • Åbo Akademi, Finland

Recent publications:

* M. Collan, M. Fedrizzi, P. Luukka, A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS-AHP framework, Expert Systems with Applications, 40 (2013), pp. 4749-4759. Matlab files: mfiles

* L. Niyigena, P. Luukka, M. Collan, Supplier evaluation with fuzzy similarity based fuzzy TOPSIS with new fuzzy similarity measure. 13th IEEE International Symposium on Computational Intelligence and Informatics. Budapest, November, 2012.

* M Collan, P Luukka, Evaluating R&D Projects as Investments by Using an Overall Ranking from Four New Fuzzy Similarity Measure Based TOPSIS Variants, IEEE transactions on fuzzy systems, 22,(3), 2014, pp. 505 - 515.

Fuzzy data analysis

In this research group people currently involved are: Pasi Luukka from LUT and Onesfole Kurama from Makerere University.

Recent publications:

* Luukka, P., Kurama, O., Similarity classifier with ordered weighted averaging operators, Expert Systems With Applications, 40 (2013), pp. 995-1002.

*Cesar Iyakaremye, Pasi Luukka, David Koloseni: Feature selection using Yu's similarity measure and fuzzy entropy measures. 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1-6. Matlab codes:M and mex files , C codes: C codes


Publications:

2011:

  • Luukka, P, Fuzzy Similarity in Multicriteria Decision-Making Problem Applied to Supplier Evaluation and Selection in Supply Chain Management, Advances in Artificial Intelligence, 2011.
  • Luukka, P, A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets, International Journal of Fuzzy Systems, 2011, vol. 13, nro. 3, p. 153-162.
  • Luukka, P, Fuzzy beans in classification, Expert Systems with Applications, 2011, vol. 38, nro. 5, p. 4798-4801.
  • Luukka, P, Feature Selection Using Fuzzy Entropy Measures with Similarity Classifier., Expert Systems with Applications, 2011, vol. 38, nro. 4, p. 4600-4607. Matlab codes:M and mex files , C codes: C codes
  • Luukka, P, & Lampinen, J, Differential Evolution Classifier in Noisy Settings and with Interacting Variables., Applied Soft Computing, 2011, vol. 11, nro. 1, p. 891-899.

2010:

  • Luukka, P, Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis, Expert Systems with Applications, 2010, vol. 37, nro. 12, p. 8296-8302.

2009:

  • Luukka, P, Similarity Classifier Using Similarities Based on Modified Probabilistic Equivalence Relations, Knowledge-Based Systems, 2009, vol. 22, nro. 1, p. 57-62.
  • Luukka, P, Classification based on fuzzy robust PCA algorithms and similarity classifier, Expert Systems with Applications, 2009, vol. 36, nro. 4, p. 7463-7468.
  • Luukka, P, PCA for fuzzy data and similarity classifier in building recognition system for postoperative patient data., Expert Systems with Applications, 2009, vol. 36, nro. 2, p. 1222-1228.
  • Luukka, P, & Mattila, J. K. Fuzzy Linear Systems Applied to Leontief Input-Output Model, Acta Technica Jaurinensis, 2009, vol. 2, nro. 2, p. 249-264.

2008:

  • Luukka, P, Similarity classifier in diagnosis of bladder cancer, Computer Methods and Programs in Biomedicine, 2008, vol. 89, nro. 1, p. 43-49.
  • Kukkurainen, P, & Luukka, P, Classification Method Using Fuzzy Level Set Subgrouping, Expert Systems with Applications, 2008, vol. 34, nro. 2, p. 859-865.

2007:

  • Luukka, P, Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets, Computers in Biology and Medicine, 2007, vol. 37, nro. 8, p. 1133-1140.

2006:

  • Luukka, P, & Leppälampi, T, Similarity Classifier with Generalized Mean Applied to Medical Data, Computers in Biology and Medicine, 2006, vol. 36, nro. 9, p. 1026-1040.
 
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