Alexey Tsymbal

Siemens (Germany)

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Seppo Puuronen

University of Jyväskylä

Mykola Pechenizkiy

Eindhoven University of Technology

Pádraig Cunningham

University College Dublin

Martin Huber

University of Defence

Matthias Hammon

Universitätsklinikum Erlangen

Dorin Comaniciu

Siemens (United States)

David Patterson

10X Genomics (United States)

S. Kevin Zhou

Suzhou University of Science and Technology

Alexander Cavallaro

Friedrich-Alexander-Universität Erlangen-Nürnberg

Vagan Terziyan

University of Jyväskylä

Cooperative Institutions

Siemens (Germany)

Siemens Healthcare (Germany)

Friedrich-Alexander-Universität Erlangen-Nürnberg

Siemens (United States)

Siemens Healthcare (United States)

Universitätsklinikum Erlangen

University of the West of England

University of Jyväskylä

Princeton University

Trinity College Dublin

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Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction

Mykola Pechenizkiy Alexey Tsymbal Seppo Puuronen O. Pechenizkiy

Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality of data and on the appropriate selection of a learning algorithm for the data. In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results in higher classification accuracy of learnt models without the separate explicit elimination of noisy instances.

Supervised Learning

Feature (linguistics)

Noisy data

10.1109/cbms.2006.65

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Citations (112)

Hill-climbing feature selection for multi-stream ASR

Interspeech 2022 (2009)

David Gelbart Nelson Morgan Alexey Tsymbal

We performed automated feature selection for multi-stream (i.e., ensemble) automatic speech recognition, using a hillclimbing (HC) algorithm that changes one feature at a time if the change improves a performance score. For both clean and noisy data sets (using the OGI Numbers corpus), HC usually improved performance on held out data compared to the initial system it started with, even for noise types that were not seen during the HC process. Overall, we found that using Opitz’s scoring formula, which blends single-classifier word recognition accuracy and ensemble diversity, worked better than ensemble accuracy as a performance score for guiding HC in cases of extreme mismatch between the SNR of training and test sets. Our noisy version of the Numbers corpus, our multi-layerperceptron-based Numbers ASR system, and our HC scripts are available online.

Feature (linguistics)

Training set

Noisy data

Ensemble Learning

10.21437/interspeech.2009-751

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Citations (6)

Local feature selection for heterogeneous problems

International Conference on Data Mining (2000)

Iryna Skrypnyk Alexey Tsymbal Seppo Puuronen

Current electronic data repositories contain enormous amount of data including also unknown and potentially interesting patterns and relations. One approach commonly used is supervised machine learning, in which a set of training instances is used to train one or more classifiers that map the space formed by different features of the instances into the set of class values. The classifiers are later used to classify new instances with unknown class values. The multidimensional data is sometimes feature-space heterogeneous so that different features have different importance in different subareas of the whole space. In this paper we describe a technique that searches for a division of the feature space identifying the best subsets of features for each instance. Our technique is based on the wrapper approach where a classification algorithm is used as the evaluation function to differentiate between several feature subsets. On the other hand, to make the feature selection dynamic or local, we apply the recently developed technique for dynamic integration of classifiers to determine what classifier and with what feature subset is applied for each new instance. Our technique can be applied in the case of implicit heterogeneity when the regions of heterogeneity cannot be easily defined by a simple dependency. We make experiments with well-known datasets of the UCI machine learning repository using ensembles of simple base classifiers. The base classifiers discussed in the experiments are developed by C4.5 based on only one feature each but the combining method itself is allowed to take into account unlimited amount of present features. The results achieved are promising and show that the local data mining in comparison with mining the whole space can be advantageous. In many cases, the mining accuracy is better as well as the time of processing is shorter, because only a small set of features is used to classify each new instance.

Linear classifier

Feature vector

Feature (linguistics)

10.2495/data000191

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Citations (2)

On the Use of Information Systems Research Methods in Datamining

Springer eBooks (2006)

Mykola Pechenizkiy Seppo Puuronen Alexey Tsymbal

Concept mining

10.1007/0-387-28809-0_42

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Citations (9)

A case-based technique for tracking concept drift in spam filtering

Knowledge-Based Systems (2005)

Sarah Jane Delany Pádraig Cunningham Alexey Tsymbal Lorcan Coyle

Concept Drift

Tracking (education)

Feature (linguistics)

Base (topology)

10.1016/j.knosys.2004.10.002

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Citations (201)

Knowledge Discovery in Microbiology Data: Analysis of Antibiotic Resistance in Nosocomial Infections

Wissensmanagement (2005)

Alexey Tsymbal Mykola Pechenizkiy Seppo Puuronen Michael Shifrin Irina V. Аlexandrova

Clinical Microbiology

Source

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Citations (3)

A Case-Based Technique for Tracking Concept Drift in Spam Filtering

Springer eBooks (2007)

Sarah Jane Delany Pádraig Cunningham Alexey Tsymbal Lorcan Coyle

Concept Drift

Tracking (education)

10.1007/1-84628-103-2_1

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Citations (28)

Feature extraction for classification in the data mining process

Mykola Pechenizkiy Seppo Puuronen Alexey Tsymbal

Dimensionality reduction is a very important step in data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of the curse of dimensionality. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning accuracy of classification schemes is presented with conclusion about search for most appropriate feature extraction method. The problem how to discover knowledge needed to integrate feature extraction and classification processes is stated. A decision support system to aid in integration of feature extraction and classification processes is proposed. The goals and requirements set for decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up proposed system are considered.

Feature (linguistics)

Source

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Citations (14)

PCA-based feature transformation for classification: issues in medical diagnostics

Mykola Pechenizkiy Alexey Tsymbal Seppo Puuronen

The goal of this paper is to propose, evaluate, and compare several data mining strategies that apply feature transformation for subsequent classification, and to consider their application to medical diagnostics. We (1) briefly consider the necessity of dimensionality reduction and discuss why feature transformation may work better than feature selection for some problems; (2) analyze experimentally whether extraction of new components and replacement of original features by them is better than storing the original features as well; (3) consider how important the use of class information is in the feature extraction process; and (4) discuss some interpretability issues regarding the extracted features.

Interpretability

Feature (linguistics)

10.1109/cbms.2004.1311770

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Citations (51)

Trends in computer-based medical systems

ACM SIGHIT Record (2012)

Paolo Soda Sameer Antani Francesco Tortorella Mario Cannataro Mykola Pechenizkiy

The 25th IEEE International Symposium on Computer-Based Medical Systems, held June 20--22, 2012 in Rome, Italy, has been a forum for discussing the latest developments in the field of computational medicine, biomedical informatics, and related disciplines. The Symposium has covered a broad range of issues in several areas with accepted submissions gathered into one general track, addressing general questions of computational biomedicine, and twelve special tracks, each focused on a particular related sub-theme. Three keynote speakers have presented an overview of recent research and developments in their particular subareas of mining clinical data for biomedical research, multivariate and multiscale analysis of biomedical signals, and oncology image analysis.

Biomedicine

Theme (computing)

10.1145/2384556.2384563

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Citations (1)