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1
Content available remote Epsilon-Insensitive Fuzzy c-Medians Clustering
100%
Łęski J.
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2002
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tom Vol. 50, Nr 4
361--374
EN
Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy C-Means (FCM) method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensivity to presence of noise and outliers in data. This paper introduces a new ε-insensitive Fuzzy C-Medians (εFCMed) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMed). Performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) and the FCMed methods using synthetic heavy-tailed and overlapped groups in background noise, and the Iris database.
2
Content available remote Kernel Ho-Kashyap classifier with generalization control
100%
Łęski J.
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2004
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tom Vol. 14, no 1
53-61
EN
This paper introduces a new classifier design method based on a kernel extension of the classical Ho-Kashyap procedure. The proposed method uses an approximation of the absolute error rather than the squared error to design a classifier, which leads to robustness against outliers and a better approximation of the misclassification error. Additionally, easy control of the generalization ability is obtained using the structural risk minimization induction principle from statistical learning theory. Finally, examples are given to demonstrate the validity of the introduced method.
3
Content available remote Minimum absolute error classifier design with generalization control
100%
Łęski J.
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tom Vol. 12, no. 3
289-299
EN
This paper introduces a new classifier design method, that is based on an extension of the classical Ho-Kashyap procedure. The proposed method uses absolute error rather than square errorto design a linear classifier. Additionally, easy control of generalization ability and outliers robustness is obtained. Finally, examples are giver to demonstrate the validity of the introduced method.
4
Content available remote Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
100%
Łęski J.
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2002
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tom Vol. 12, no 3
437-447
EN
A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
5
Content available remote Robust possibilistic clustering
100%
Łęski J.
Archives of Control Sciences
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2000
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tom Vol. 10, no. 3/4
141-155
EN
Fuzzy and possibilistic clustering helps to find natural vague boundaries in data and has long been a popular unsupervised learning method. The Fuzzy C-Means (FCM) method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in data. The FCM applies the constraint that the memberships of each datum across groups sum to 1. Due to this constraint and L/sub 2/ norm as the dissimilarity measure, the FCM has considerable trouble in a noisy environment. In possibilistic C-means (PCM) the above constraint is not used. In this case membership values may be interpreted as degrees of possibility that the datum belongs to the groups. In the possibilistic approach still L/sub 2/ norm is usually used and the second reason of sensitivity for outliers and noise remains. This paper introduces a new epsilon -insensitive Possibilistic C-Means ( epsilon PCM) clustering algorithm. The performance of the new clustering algorithm is experimentally compared with the PCM method using simple two-dimensional synthetic data with outliers and the real-world Iris database.
6
Content available Iteratively reweighted least squares classifier and its l2- and l1-regularized Kernel versions
100%
Łęski J.
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nr 1
7
Content available Generalized ordered linear regression with regularization
63%
Łęski J. , Henzel N.
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2012
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tom Vol. 60, nr 3
481-489
EN
Linear regression analysis has become a fundamental tool in experimental sciences. We propose a new method for parameter estimation in linear models. The 'Generalized Ordered Linear Regression with Regularization' (GOLRR) uses various loss functions (including the o-insensitive ones), ordered weighted averaging of the residuals, and regularization. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend not only on the values but also on the order of the model residuals obtained for the current iteration. Such regression problem may be transformed into the iterative reweighted least squares scenario. The conjugate gradient algorithm is used to minimize the proposed criterion function. Finally, numerical examples are given to demonstrate the validity of the method proposed.
8
Content available remote A Neuro-Fuzzy System Using Logical Interpretation of If-Then Rules and its Application to Diabetes Mellitus Forecasting
63%
Łęski J. , Henzel N.
Archives of Control Sciences
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1999
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tom Vol. 9, no. 1/2
107-122
EN
Initially, an axiomatic definition of fuzzy implication has been recalled. Next, several important fuzzy implications and their properties have been described. Then, the idea of approximate reasoning using generalized modus ponens and fuzzy implication are considered. After reviewing well-known fuzzy systems, a new Artificial Neural network Based on Logical Interpretation of if-then Rules (ANBLIR) is introduced. The elimination of non-informative part of final fuzzy set before defuzzification plays the pivotal role in this system. The application of ANBLIR to recognition of non-insulin-dependent diabetes mellitus in Pima Indians is shown.
9
Content available Empirical Bayesian averaging method and its application to noise reduction in ECG signal
51%
Momot A. , Momot M. , Łęski J.
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tom Vol. 10
93--101
EN
An electrocardiogram (ECG) is the prime tool in non-invasive cardiac electrophysiology and has a prime function in the screening and diagnosis of cardiovascular diseases. However one of the greatest problems is that usually recording an electrical activity of the heart is performed in the presence of noise. The paper presents empirical Bayesian approach to problem of signal averaging which is commonly used to extract a useful signal distorted by a noise. The averaging is especially useful for biomedical signal such as ECG signal, where the spectra of the signal and noise significantly overlap. In reality the variability of noise can be observed, with power from cycle to cycle, which is motivation for weighted averaging methods usage. It is demonstrated that by exploiting a probabilistic Bayesian learning framework, it can be derived accurate prediction models offering significant additional advantage, namely automatic estimation of 'nuisance' parameters. Performance of the new method is experimentally compared to the traditional averaging by using arithmetic mean and weighted averaging method based on criterion function minimization.
10
Content available Weighted averaging of ECG signals based on partition of input set in time domain
51%
Momot A. , Momot M. , Łęski J.
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2007
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tom Vol. 11
165--170
EN
The paper presents new approach to problem of signal averaging which is commonly used to extract a useful signal distorted by a noise. The averaging is especially useful for biomedical signal such as ECG signal, where the spectra of the signal and noise significantly overlap. In reality can be observed variability of noise power from cycle to cycle which is motivation for using methods of weighted averaging. Performance of the new method, based on partition of input set in time domain and criterion function minimization, is experimentally compared with the traditional averaging by using arithmetic mean, weighted averaging method based on empirical Bayesian approach and weighted averaging method based on criterion function minimization.
11
Content available Szereg czasowy zbiorów rozmytych w opisie i analizie sygnałów elektrokardiograficznych
51%
Henzel N. , Gacek A. , Łęski J.
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2011
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tom Vol. 17, nr 4
313-316
PL
W pracy przedstawiono ujednolicenie metod opisu sygnałów elektrokardiograficznych za pomocą koncepcji szeregu czasowego zbiorów rozmytych. Sygnał elektrokardiograficzny jest przetwarzany w "ruchomym oknie" czasowym i przekształcany na szereg czasowy funkcji przynależności zbiorów rozmytych. Jako szczególne przypadki wprowadzonej metody można rozważać koncepcję sygnału rozmytego oraz ciąg ziaren informacyjnych.
EN
The unification of the electrocardiography signals description methods by means of time series of fuzzy sets, is presented. The electrocardiographic signal is processed in a “moving window” and transformed into a time series of fuzzy sets membership functions. The idea of fuzzy signal and the sequence of information granules can be considered as special case of the presented method.
12
Content available The Fuzzy Relevance Vector Machine and its application to noise reduction in ECG signal
51%
Momot A. , Momot M. , Łęski J.
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2005
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tom Vol. 9
99--105
EN
The paper presents new method called the Fuzzy Relevance Vector Machine (FRVM), a modification of the relevance vector machine, introduced by M. Tipping, applied to learning Takagi-Sugeno-Kang (TSK) fuzzy system. Moreover it describes application of the FRVM to noise reduction in ECG signal. The results of the process are compared to those obtained using both Least Squares method for learning output functions in TSK rules and commonly used method using a low-pass moving average filter.
13
Content available Bayesian and empirical Bayesian approach to weighted averaging of ECG signal
51%
Momot A. , Momot M. , Łęski J.
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nr 4
14
Content available The prediction of the low fetal birth weight based on quantitative description of cardiotocographic signals
38%
Czabański R. , Jeżewski M. , Wróbel J. , Kupka T. , Łęski J. , Jeżewski J.
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tom Vol. 12
97--102
EN
Cardiotocography (CTG) is a routine method of fetal condition assessment used in modern obstetrics. It is a biophysical method based on simultaneous recording and analysis of activity of fetal heart, fetal movements and maternal uterine contractions. The fetal condition is diagnosed on the basis of printed CTG trace evaluation. The correct interpretation of CTG traces from a bedside monitor is very difficult even for experienced clinicians. Therefore, computerized fetal monitoring systems are used to yield the quantitative description of the signal. However, the effective methods, aiming to support the conclusion generation, are still being searched. One of the most important features defining the state of fetal outcome is the weight of the newborn. The presented work describes an application of the Artificial Neural Network Based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) to evaluate the risk of the low birth weight using a set of parameters quantitatively describing the CTG traces. The obtained results confirm that the neuro-fuzzy based CTG classification methods are very efficient for the prediction of the fetal outcome.
15
Content available Influence of gestational age on neural networks interpretation of fetal monitoring signals
38%
Jeżewski M. , Czabański R. , Horoba K. , Wróbel J. , Łęski J. , Jeżewski J.
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tom Vol. 12
137--142
EN
Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.
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