Kalle Saastamoinen

A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure

The aim of this paper is to introduce improvements made to a classifier based on maximal fuzzy similarity. Improvements are based on the use of generalized Lukasiewicz-structure and weight optimization. The main benefits of the classifier are its computational efficiency and its strong mathematical background. It is based on many-valued logic and it provides semantic information about classification results. We show that if one chooses the power value in a right manner in the generalized Lukasiewicz-structure and the optimal weights for different feature, one can see significant enhancements in classification results.

Medical Data Classification using Logical Similarity Based Measures

In this article we study what kind of results can be established using logical similarity measures in classification of some well-known medical data. New approach for the detection of liver disorders, thyroid, diabetes and breast cancer. We compare our results to some known results and show that these logical comparison measures are able to give better results

A classifier based on the fuzzy similarity in the Lukasiewicz structure with different metrics

The first aim of this paper is to extend the fuzzy similarity relation defined in the generalized Lukasiewicz structure to utilize common and cumulative Minkowsky metrics and introduce a new classifier based on the cumulative similarity measure which uses the Minkowsky metrics in the generalized Lukasiewicz structure. The second aim of this paper is to study properties of this classifier when applied to three different datasets.

Testing continuous t-norm called Lukasiewicz algebra with different means in classification

In this paper, we have done new similarity measures from a continuous t-norm by implementing it in different mean measures. For the implementation, we use a Minkowsky metric based on Lukasiewicz algebra. We test these new similarities in both the generalised and normal form of Lukasiewicz algebra with weight optimisation. The mean measures examined here are arithmetic, geometric and harmonic means. We show that the magnitude order of the similarities are S/sub H//sup N/ /spl ges/S/sub G//sup N/ /spl ges/S/sub A//sup N/ . Secondly, we show that the use of different means is highly recommendable in some cases.

Defining athlete's anaerobic and aerobic thresholds by using similarity measures and differential evolution

This paper presents a computational model for defining an athletes aerobic and anaerobic thresholds. The model simulates the decision-making done by specialists in sport medicine. Computation is based on fuzzy decision making using expert criteria. Decision-making means in this paper the use of similarity measures, generalized mean, fuzzy membership functions and differential evolution. We have used differential evolution to tune parameters in our comparison measure based on similarities combined with generalized mean. The comparison measure is used to combine our models fuzzified criterions, which originally came from experts. Simulation results show that this model succeeds in finding thresholds, which does not differ statistically significantly from the thresholds estimated by human sport medicine experts.

Semantic study of the use of parameterized S implications and equivalences in comparison

In this article, we will create some new classes of parameterized equivalences that we get from the different meanings of the fuzzy conjunction operators. We will concentrate on studying the class called S implications and equivalences, which rise from this class. We will present measures on the basis of the use of implications and equivalences with generalized mean operators. Test results concerning corresponding implications versus equivalences are established using an instance-based classification method. It is shown that the measures established by parameterization presented in this article will give very good results and that the equivalences are more suitable for classification than corresponding implications.

On the use of generalized mean with t-norms and t-conorms

In this article we will present two new generalized classes of t-norm- and t-conorm-based generalized means with weights. We are going to test the usability of these measures which we get by combining the classes of t-norms and t-conorms with the generalized mean operator. Since we are using the generalized mean we have an additional parameter that controls the power, which the argument values are raised. The Dombi and Yager type classes of fuzzy conjunctions and disjunctions are used as examples.

Error-free simplification of transparent Mamdani systems

This paper shows that combinatorial complexity of fuzzy systems is at least in part caused by redundancy in these systems and presents the algorithm and its implementation for detection and removal of such redundancy for a special class of Mamdani systems. Performance of the simplification algorithm is demonstrated with uniformly impressive results on acknowledged benchmarks coming from different areas of engineering - truck backer-upper control, Mackey-Glass time series prediction and iris data classification.

On New General Class of 3π- Uninorms

In this article we are presenting new class of uninorms starting from the 3π-uninorm. We give proof that this new class of the parameterized 3π-uninorm still holds all the properties of uninorms. We also add this new measure generalized mean and weights. We add this comparison measure into the typical instance based classification procedure and show that this new comparison measure gives more flexibility for results achieved than simple 3pi-uninorm gives. Comparison with some known classification results with different classifiers is also added in practical part of this article.

Use of hybrid method in wavelet base selection for signal compression

In this paper we study a problem of signal compression how to choose a best mother wavelet from the set S of wavelets. The approach is following: First we calculate a discrete wavelet transform of signal by using one standard wavelet. Then we form coefficients mi for each scale i from the wavelet expansions coefficients. Coefficients mi are used for selecting best wavelet from the set S. Selection is classification problem and we have constructed classification algorithm that uses fuzzy similarity that is based on a continuous t-norm called Lukasiewicz algebra. We are using normal and cumulative forms of generalized Lukasiewicz algebra and we have also applied a genetic algorithm into the our classifier to choose appropriate weights in our classification tasks. There are many advantages what we get by using t-norm called Lukasiewicz in classification: 1) Structure has a promising mathematical background 2) Mean of many fuzzy similarities is still a fuzzy similarity 3) Any pseudo-metric induces fuzzy similarity on a given non-empty set X with respect to the Lukasiewicz-conjunction. Algorithm is efficient especially because we have to calculate wavelet transform only once and classification is simple and fast. Algorithm is also very flexible, cause we can implement any type metrics or mean measures into it. As our results we will present a new method to select best mother wavelet from a given set S. We will also show that proposed hybrid method can be used in this kind of analytical problems. The best way to form coefficients mi and choose metric or measure is depended of class of signals we are working with, which is still unclear.