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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. 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.
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Vladimir Stanovov, Eugene Semenkin and Olga Semenkina
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.
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.
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.
Madara Gasparoviсa, Ludmila Aleksejeva and Valdis Gersons
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Madara Gasparovica, Natalia Novoselova and Ludmila Aleksejeva
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Yevgeniy Bodyanskiy, Olena Vynokurova, Ilya Kobylin and Oleg Kobylin
. Kolodyazhniy, “Evolving fuzzyclassification 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.
 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