The concept and data drift problems have received much attention in recent years. This aspect is ... more The concept and data drift problems have received much attention in recent years. This aspect is crucial in many domains exhibiting non-stationary and cyclical patterns affecting their generative processes. Drift detection can be treated as a supervised task, with labeled data constantly used to validate the learned model. From a practical point of view, this is an impractical task because labeling is complex, costly, and time-consuming. On the other hand, unsupervised change detection techniques are cumbersome in applications because they generate many false alarms. The paper presents a new concept drift detection method based on feature analysis. Stream of data carries information about the distribution patterns that reflect different concepts that may be hidden in the data. The essential features are searched and ranked by LASSO. The rank of features and statistics are employed to feature drift detection. The proposed approach was experimentally checked based on synthetic and nat...
Keystroke Dynamics - database contains free-typing keystroke dynamics, correlated with user IDs. ... more Keystroke Dynamics - database contains free-typing keystroke dynamics, correlated with user IDs. 15000 records for keystroke dynamics. 150 users, 100 records have been obtained per user. Data of user "X" is in file "user_X". Each row of the file represents a user keystroke dynamics vector, each column represents one feature attribute. The last column contains class labels.
Journal of Medical Informatics and Technologies, 2012
The study being presented is a continuation of the previous studies that consisted in the adaptat... more The study being presented is a continuation of the previous studies that consisted in the adaptation and use of the Levenshtein method in a signature recognition process. Three methods based on the normalized Levenshtein measure were taken into consideration. The studies included an analysis and selection of appropriate signature features, on the basis of which the authenticity of a signature was verified later. A statistical apparatus was used to perform a comprehensive analysis. The independence test χ 2 was applied. It allowed determining the relationship between signature features and the error returned by the classifier.
Advances in intelligent systems and computing, May 8, 2019
Common problem in data classification is the incompleteness of the data, and not always it is pos... more Common problem in data classification is the incompleteness of the data, and not always it is possible to re-acquire the missing values. Another approach is to fill-in missing values using some statistical methods. This however distracts the original data and may lead to over-fit the classifier to the artificially generated values, and in consequence to overestimate the classifier accuracy in Cross Validation tests. In this paper we propose a solution where, for a reference data consisting of complete and incomplete records, complete records serve as a reference data for a standard classifier, while the whole set serves as a reference data for single feature subspaced classifier.
Please cite this article in press as: Porwik P, et al. Feature projection k-NN classifier model f... more Please cite this article in press as: Porwik P, et al. Feature projection k-NN classifier model for imbalanced and incomplete medical data.
An ensemble learning approach to lip-based biometric verification, with a dynamic selection of cl... more An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers, Expert Systems With Applications (2018), doi:
Journal of Medical Informatics and Technologies, 2006
In this paper the off-line type signature analysis have been presented. The signature recognition... more In this paper the off-line type signature analysis have been presented. The signature recognition is composed of some features. Different influences of such features were tested and stated. Proposed approach gives good signature recognition level, hence described method can be used in many areas, for example in biometric authentication, as biometric computer protection or as method of the analysis of person's behaviour changes.
Advances in intelligent systems and computing, Nov 3, 2015
Liver fibrosis is a common disease of the European population (but not only them). It may have ma... more Liver fibrosis is a common disease of the European population (but not only them). It may have many backgrounds and may develop with a different rapidity—it may stay hidden for many years or rapidly develop into terminal stage called cirrhosis, where liver can no longer fulfill its function. Unfortunately, current methods of diagnosis are either connected with a potential risk for a patient and require a hospitalization or are expensive and not very accurate. This paper presents a comparative study of various feature selection algorithms combined with selected machine learning algorithms which may be used to build an advanced liver fibrosis diagnosis support system based on a nonexpensive and safe routine blood tests. Experiments carried out on a dataset collected by authors, proved usability and satisfactory accuracy of the presented algorithms.
The indoor laboratory-scale microalgae culture was elevated to 50 liter in outdoor photobioreacto... more The indoor laboratory-scale microalgae culture was elevated to 50 liter in outdoor photobioreactors for the cultivation of an oil-rich microalga, Chlorella vulgaris ESP-31. Under phototrophic and photoheterotrophic conditions using CO2 and acetic acid as carbon source, respectively , the lipid productivity and lipid content obtained were 30-31 mg/L/d and 35-44%, respectively. The effect of inoculum sizes on the performance of biomass and lipid production under phototrophic cultivation was also investigated. The lipid productivity of C. vulgaris ESP-31 was further improved to 47.6 mg/L/d when a higher inoculum size of 0.70 g/L was used. Finally, to avoid severe contamination problem during outdoor cultivation in the summer time, a two-stage lipid accumulation strategy was employed. The results show that the proposed two-stage process was able to avoid the bacterial contamination and enhanced the lipid content and lipid productivity of Chlorella vulgaris ESP-31.
Engineering Applications of Artificial Intelligence, Nov 1, 2018
Classifiers are used in a variety of applications, among them the classification of medical data.... more Classifiers are used in a variety of applications, among them the classification of medical data. Their efficiency depends on the quality of training data, which is a disadvantage in the case of medical data that are often imperfect (e.g. incomplete, imbalanced, uncertain). Moreover, numerous classifiers are black-boxes from the perspective of diagnosticians who perform the final diagnoses. These drawbacks degrade the potential usefulness of classifiers in diagnosis support. A rule-based reasoning may overcome these mentioned limitations. We introduce both a rule selection and a diagnosis support method based on the Dempster-Shafer and fuzzy set theories. The theories can manage an interpretation of incomplete and imbalanced data, imprecision of medical information and knowledge uncertainty. The usefulness of the method will be proven on a test case of liver fibrosis diagnosis. The liver fibrosis stage is difficult to recognize even for experienced physicians. The diagnosis of the liver state by an invasive biopsy is ambiguous and dependent on its finite precision. Therefore, knowledge-based methods are being sought to reduce the need of invasive testing. We use a real medical database related to patients affected by hepatitis C virus to extract knowledge. The database has missing and outlying values and patients' diagnoses are uncertain. The proposed methods provide simple diagnostic rules that are helpful in this study of liver fibrosis and in processing deficient data. The greatest benefit and novelty of the approach is the ability to assess three stages of fibrosis in a non-invasive way, whereas other medical tests allow to detect only the last stage, i.e. the cirrhosis.
Journal of Medical Informatics and Technologies, 2014
Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms i... more Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms is presented in this paper. Fetal monitoring signals are provided by a mobile instrumentation consisting of bioelectrical signal recorder and tablet PC which retrieves and processes the data as well as provides wireless data transmission based on Internet. The fetal surveillance system enables analysis, dynamic presentation and archiving of acquired signals and medical data. Novelty of the proposed approach relies on smart fitting of the algorithms for analysis of the abdominal signals in mobile instrumentation, as well as on controlling of the fetal monitoring session from the surveillance center. These actions are performed automatically through continuous analyzing of the signal quality and the reliability of the quantitative parameters determined for the acquired signals. Using that approach the amount and content of data transmitted through remote channels to the surveillance center can be controlled to ensure the most reliable assessment of the fetal well-being.
The concept and data drift problems have received much attention in recent years. This aspect is ... more The concept and data drift problems have received much attention in recent years. This aspect is crucial in many domains exhibiting non-stationary and cyclical patterns affecting their generative processes. Drift detection can be treated as a supervised task, with labeled data constantly used to validate the learned model. From a practical point of view, this is an impractical task because labeling is complex, costly, and time-consuming. On the other hand, unsupervised change detection techniques are cumbersome in applications because they generate many false alarms. The paper presents a new concept drift detection method based on feature analysis. Stream of data carries information about the distribution patterns that reflect different concepts that may be hidden in the data. The essential features are searched and ranked by LASSO. The rank of features and statistics are employed to feature drift detection. The proposed approach was experimentally checked based on synthetic and nat...
Keystroke Dynamics - database contains free-typing keystroke dynamics, correlated with user IDs. ... more Keystroke Dynamics - database contains free-typing keystroke dynamics, correlated with user IDs. 15000 records for keystroke dynamics. 150 users, 100 records have been obtained per user. Data of user "X" is in file "user_X". Each row of the file represents a user keystroke dynamics vector, each column represents one feature attribute. The last column contains class labels.
Journal of Medical Informatics and Technologies, 2012
The study being presented is a continuation of the previous studies that consisted in the adaptat... more The study being presented is a continuation of the previous studies that consisted in the adaptation and use of the Levenshtein method in a signature recognition process. Three methods based on the normalized Levenshtein measure were taken into consideration. The studies included an analysis and selection of appropriate signature features, on the basis of which the authenticity of a signature was verified later. A statistical apparatus was used to perform a comprehensive analysis. The independence test χ 2 was applied. It allowed determining the relationship between signature features and the error returned by the classifier.
Advances in intelligent systems and computing, May 8, 2019
Common problem in data classification is the incompleteness of the data, and not always it is pos... more Common problem in data classification is the incompleteness of the data, and not always it is possible to re-acquire the missing values. Another approach is to fill-in missing values using some statistical methods. This however distracts the original data and may lead to over-fit the classifier to the artificially generated values, and in consequence to overestimate the classifier accuracy in Cross Validation tests. In this paper we propose a solution where, for a reference data consisting of complete and incomplete records, complete records serve as a reference data for a standard classifier, while the whole set serves as a reference data for single feature subspaced classifier.
Please cite this article in press as: Porwik P, et al. Feature projection k-NN classifier model f... more Please cite this article in press as: Porwik P, et al. Feature projection k-NN classifier model for imbalanced and incomplete medical data.
An ensemble learning approach to lip-based biometric verification, with a dynamic selection of cl... more An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers, Expert Systems With Applications (2018), doi:
Journal of Medical Informatics and Technologies, 2006
In this paper the off-line type signature analysis have been presented. The signature recognition... more In this paper the off-line type signature analysis have been presented. The signature recognition is composed of some features. Different influences of such features were tested and stated. Proposed approach gives good signature recognition level, hence described method can be used in many areas, for example in biometric authentication, as biometric computer protection or as method of the analysis of person's behaviour changes.
Advances in intelligent systems and computing, Nov 3, 2015
Liver fibrosis is a common disease of the European population (but not only them). It may have ma... more Liver fibrosis is a common disease of the European population (but not only them). It may have many backgrounds and may develop with a different rapidity—it may stay hidden for many years or rapidly develop into terminal stage called cirrhosis, where liver can no longer fulfill its function. Unfortunately, current methods of diagnosis are either connected with a potential risk for a patient and require a hospitalization or are expensive and not very accurate. This paper presents a comparative study of various feature selection algorithms combined with selected machine learning algorithms which may be used to build an advanced liver fibrosis diagnosis support system based on a nonexpensive and safe routine blood tests. Experiments carried out on a dataset collected by authors, proved usability and satisfactory accuracy of the presented algorithms.
The indoor laboratory-scale microalgae culture was elevated to 50 liter in outdoor photobioreacto... more The indoor laboratory-scale microalgae culture was elevated to 50 liter in outdoor photobioreactors for the cultivation of an oil-rich microalga, Chlorella vulgaris ESP-31. Under phototrophic and photoheterotrophic conditions using CO2 and acetic acid as carbon source, respectively , the lipid productivity and lipid content obtained were 30-31 mg/L/d and 35-44%, respectively. The effect of inoculum sizes on the performance of biomass and lipid production under phototrophic cultivation was also investigated. The lipid productivity of C. vulgaris ESP-31 was further improved to 47.6 mg/L/d when a higher inoculum size of 0.70 g/L was used. Finally, to avoid severe contamination problem during outdoor cultivation in the summer time, a two-stage lipid accumulation strategy was employed. The results show that the proposed two-stage process was able to avoid the bacterial contamination and enhanced the lipid content and lipid productivity of Chlorella vulgaris ESP-31.
Engineering Applications of Artificial Intelligence, Nov 1, 2018
Classifiers are used in a variety of applications, among them the classification of medical data.... more Classifiers are used in a variety of applications, among them the classification of medical data. Their efficiency depends on the quality of training data, which is a disadvantage in the case of medical data that are often imperfect (e.g. incomplete, imbalanced, uncertain). Moreover, numerous classifiers are black-boxes from the perspective of diagnosticians who perform the final diagnoses. These drawbacks degrade the potential usefulness of classifiers in diagnosis support. A rule-based reasoning may overcome these mentioned limitations. We introduce both a rule selection and a diagnosis support method based on the Dempster-Shafer and fuzzy set theories. The theories can manage an interpretation of incomplete and imbalanced data, imprecision of medical information and knowledge uncertainty. The usefulness of the method will be proven on a test case of liver fibrosis diagnosis. The liver fibrosis stage is difficult to recognize even for experienced physicians. The diagnosis of the liver state by an invasive biopsy is ambiguous and dependent on its finite precision. Therefore, knowledge-based methods are being sought to reduce the need of invasive testing. We use a real medical database related to patients affected by hepatitis C virus to extract knowledge. The database has missing and outlying values and patients' diagnoses are uncertain. The proposed methods provide simple diagnostic rules that are helpful in this study of liver fibrosis and in processing deficient data. The greatest benefit and novelty of the approach is the ability to assess three stages of fibrosis in a non-invasive way, whereas other medical tests allow to detect only the last stage, i.e. the cirrhosis.
Journal of Medical Informatics and Technologies, 2014
Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms i... more Telemedical system for fetal home monitoring with smart selection of signal analysis algorithms is presented in this paper. Fetal monitoring signals are provided by a mobile instrumentation consisting of bioelectrical signal recorder and tablet PC which retrieves and processes the data as well as provides wireless data transmission based on Internet. The fetal surveillance system enables analysis, dynamic presentation and archiving of acquired signals and medical data. Novelty of the proposed approach relies on smart fitting of the algorithms for analysis of the abdominal signals in mobile instrumentation, as well as on controlling of the fetal monitoring session from the surveillance center. These actions are performed automatically through continuous analyzing of the signal quality and the reliability of the quantitative parameters determined for the acquired signals. Using that approach the amount and content of data transmitted through remote channels to the surveillance center can be controlled to ensure the most reliable assessment of the fetal well-being.
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Papers by Piotr Porwik