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A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

Abstract

Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

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A Study on the Behaviour of the Algorithm for Finding Relevant Attributes and Membership Functions

. Aleksejeva K. Makejeva [u.c.]. - Rīga: SIA Drukātava, 2007, 120 lpp. Bramer M. Principles of Data Mining. - London: Springer, 2007, 343 p. Cendrowska J. PRISM: an Algorithm for Inducing Modular Rules. Internat. J. Man Machine Stud. - Vol. 27 (1987), pp. 349-370. Chen S.-M., Fang Y.-D. A New Method to Deal with Fuzzy Classification Problems by Tuning Membership Functions for Fuzzy Classification Systems. Journal of the Chinese Institute of Engineers. - Vol. 28, No.1 (2005), pp. 169-173.

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Fuzzy Classification System for Bioinformatics Data Analysis/ Izplūdusī klasifikācijas sistēma bioinformātikas datu analīzei/ Система нечеткой классификации данных биоинформатики

. Aleksejeva, V. Nazaruks, “Using Fuzzy Clustering with Bioinformatics Data,” Proceedings of the 6th International Conference on Applied Information and Communication Technologies, AICT2013, Apr. 25-26, 2013, pp. 62-70. Jelgava, Latvia. [13] M. Gasparoviča, “Using Fuzzy Algorithms to Solve Classification Tasks,” Master thesis, Riga technical University, Riga. Latvia, 2010 (in Latvian) [14] J. Zyl, “Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules,” PhD thesis, University of Manheim, Germany, 2007

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Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

rule-based classifier, In Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013), pages 61–66, Milan, Italy, 2013 [30] L. Rokach, Pattern Classification Using Ensemble Methods, volume 75 of Series in Machine Perception and Artifical Intelligence, World Scientific Publishing Company, Singapore, 2010 [31] H. Roubos, M. Setnes, and J. Abonyi, Learning fuzzy classification rules from data, Information Sciences, 150(1–2):77–93, 2003 [32] J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S

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Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Abstract

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

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A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

Abstract

Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

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On classification with missing data using rough-neuro-fuzzy systems

On classification with missing data using rough-neuro-fuzzy systems

The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

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The Use of BEXA Family Algorithms in Bioinformatics Data Classification

-203. [4] P. Clark. The CN2 Induction Algorithm / Clark P. and Niblett T. // Machine Learning. Vol. 3, 1989, pp. 261-283. [5] J. Hong. AQ15: Incremental Learning of Attribute-Based Descriptions from Examples the Method and User Guide. Report of the Intelligent Systems Group, UIUCDCS-F-86-949 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 1986. [6] J. Zyl. Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules. - Mannheim: PhD thesis, 2007. p 263. [7] A

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Using Fuzzy Logic to Solve Bioinformatics Tasks

," Physiological Genomics, vol. 13, pp.107-117, 2002. L. Ohno- Machado, S. Vinterbo, G. Weber , "Classiication of Gene Expression Data Using Fuzzy Logic," J. Intell. Fuzzy Syst., vol. 12, pp. 19-24, 2002. S. A. Vinterbo, E.-Y. Kim, L. Ohno-Machado , "Small, fuzzy and interpretable gene expression based classifiers," Bioinformatics, vol. 21, no. 9, pp. 1964-1970, 2005. M. H. Marghny, E. El-Semman , "Extracting fuzzy classification rules with gene expression programming," presented at AIML 05 Conference

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Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks

. Kolodyazhniy, “Evolving fuzzy classification of non-stationary time series,” in Evolving Intelligent Systems: Methodology and Applications, P. Angelov, D. Filev, N. Kasabov Ed., NY: John Wiley & Sons, 2008, pp. 446-464. [8] C. S. Möller-Levet, F. Klawonn, K.-H. Cho and O. Wolkenhauer, “Fuzzy clustering of short time series and unevenly distributed sampling points,” in Advances in Intelligent Data Analysis V (Lecture Notes in Computers Science), Vol. 2810, Heidelberg: Springer, 2003, pp. 330-340. https://doi.org/10.1007/978-3-540-45231-7_31 [9

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