Aleš Pilný

My photographAs a Ph.D. student joined the group in 2008 after master degree at FEE, CTU Prague. His research is focused on Feature Ranking and Feature Selection methods and its application in Data Mining.



  • Aleš Pilný, Wolfgang Oertel, Pavel Kordík, Miroslav Šnorek: Correlation-based Feature Ranking in Combination with Embedded Feature Selection. vol. nr. , p. , , 2009. ISSN BibTex, PDF

    Most of Feature Ranking and Feature Selection approaches can be used for categorial data only. Some of them rely on statistical measures of the data, some are tailored to a specific data mining algorithm (wrapper approach). In this paper we present new methods for feature ranking and selection obtained as a combination of the above mentioned approaches. The data mining algorithm (GAME) is designed for numerical data, but it can be applied to categorial data as well. It incorporates feature selection mechanisms and new methods, proposed in this paper, derive feature ranking from final data mining model. The rank of each feature selected by model is computed by processing correlations of outputs between neighboring model’s neurons in different ways. We used four different methods based on fuzzy logic, certainty factors and simple calculus. The performance of these four feature ranking methods was tested on artificial data sets, on well known Ionosphere data set and on well known Housing data set with continuous variables. The results indicated that the method based on simple calculus approach was significantly worse than other three methods. These methods produce ranking consistent with recently published studies.

  • NOVÁK D., PILNÝ A., KORDÍK P., HOLIGA Š., POŠÍK P., ČERNÝ R., BRZEZNÝ R.: Analysis of Vestibular-Ocular Reflex by Evolutionary Framework. vol. nr. , p. 452-461, Springer, 2008. ISSN BibTex, PDF

    In this paper the problem of analysis of eye movements using sinu- soidal head rotation test is presented. The goal of the method is to discard au- tomatically the effect of the fast phase-saccades and consequently calculate the response of vestibular system in the form of phase shift and amplitude. The com- parison of threshold detection and inductive models trained on saccades is car- ried out. After saccades detection we are left with discontinuous signal segments. This paper presents an approach to align them to form a smooth signal with the same frequencies that were originally present in the source signal. The approach is based on a direct estimation of the signal component parameters using the evolutionary strategy with covariance matrix adaptation. The performance of evolutionary approach is compared to least-square multimodal sinus fit. The experimental evaluation on real-world signals revealed that threshold saccades detection with combination of the evolutionary strategy is robust, scalable and reliable method

  • : Behaviour of FeRaNGA Method for Feature Ranking During Learning Process Using Inductive Modelling. Proceedings of the 2nd International Conference on Inductive Modelling. Kiev: Ukr. INTEI vol. nr. , p. , , 2008. ISSN BibTex, PDF

    Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process. Processing elements transforms parent input features to an output. The selection process is realized by means of NGA where units connected to the least significant features starve and fade from population. To obtain the best results and to find optimal configuration is behaviour of the FeRaNGA algortithm tested using various parameters of NGA and number of ensemble GAME models on well known artificial data sets.

  • Ales Pilny, Pavel Kordik, Miroslav Snorek: Feature Ranking Derived from Data Mining Process. In Artificial Neural Networks - ICANN 2008, 18th International Conference Proceedings vol. nr. , p. , Heidelberg: Springer,, 2008. ISSN BibTex, PDF

    Most common feature ranking methods are based on the sta- tistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that sur- vived the selection process. A child unit is a processing element trans- forming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new feature ranking algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets.

  • Aleš Pilný, Pavel Kordík: Reconstruction of Eye Movements Signal using Inductive Model Detecting Saccades. vol. 1 nr. , p. , Czech Technical University, 2007. ISSN BibTex, PDF

    This article describes a method for reconstruction of eye movement signals interfered with saccades and post-determination of inherent frequencies in the signal. For healthy patients, a signal of their eye movements should contain the same frequencies as movements generated by special rotating chair. To determine frequencies in eye movements, saccades have to be removed first. This is not an easy task, because saccades can have various shapes. To detect saccades, we use inductive models trained on various saccadic eye movement signals. To remove saccades and to reconstruct the eye movement signal we wrote special script replacing saccades with estimated trend of signal based on the output of the inductive model. When the reconstructed signal is transformed to the frequency domain, it is easy to decide, whether the eye movements signal contains the same frequencies as the original signal of the rotating chair.