| 1. |
- Alzghoul, Ahmad, et al.
(author)
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Screening paper runnability in a web-offset pressroom by data mining
- 2009
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In: Proceedings of the 9th Industrial Conference on Advances in Data Mining. - Berlin : Springer Berlin/Heidelberg. - 9783642030666 ; , s. 161-175
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Conference paper (peer-reviewed)abstract
- This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.
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| 2. |
- Bacauskiene, Marija, et al.
(author)
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A feature selection technique for generation of classification committees and its application to categorization of laryngeal images
- 2009
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In: Pattern Recognition. - New York : Pergamon Press. - 0031-3203 .- 1873-5142. ; 42:5, s. 645-654
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Journal article (peer-reviewed)abstract
- This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in Classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.
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| 3. |
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| 4. |
- Bacauskiene, Marija, et al.
(author)
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Random forests based monitoring of human larynx using questionnaire data
- 2012
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In: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 5506-5512
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Journal article (peer-reviewed)abstract
- This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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| 5. |
- Bacauskiene, Marija, et al.
(author)
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Selecting salient features for classification based on neural network committees
- 2004
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In: Pattern Recognition Letters. - Amsterdam : Elsevier Science. - 0167-8655 .- 1872-7344. ; 25:16, s. 1879-1891
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Journal article (peer-reviewed)abstract
- Aggregating outputs of multiple classifiers into a committee decision is one of the most important techniques for improving classification accuracy. The issue of selecting an optimal subset of relevant features plays also an important role in successful design of a pattern recognition system. In this paper, we present a neural network based approach for identifying salient features for classification in neural network committees. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. The accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features.
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| 6. |
- Bacauskiene, Marija, et al.
(author)
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Selecting variables for neural network committees
- 2006
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In: Advances in neural networks - ISNN 2006. - Berlin : Springer Berlin/Heidelberg. - 9783540344391 ; , s. 837-842
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Conference paper (peer-reviewed)abstract
- The aim of the variable selection is threefold: to reduce model complexity, to promote diversity of committee networks, and to find a trade-off between the accuracy and diversity of the networks. To achieve the goal, the steps of neural network training, aggregation, and elimination of irrelevant input variables are integrated based on the negative correlation learning [1] error function. Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed.
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| 7. |
- Bacauskiene, Marija, et al.
(author)
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The Evidence Theory Based Post-Processing of Colour Images
- 2004
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In: Informatica (Vilnius). - Vilnius : Institute of Mathematics and Cybernetics, Lithuanian Academy of Sciences. - 0868-4952 .- 1822-8844. ; 15:3, s. 315-328
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Journal article (peer-reviewed)abstract
- The problem of post-processing of a classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. A post-processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.
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| 8. |
- Gelzinis, Adas, et al.
(author)
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A novel technique to extract accurate cell contours applied to analysis of phytoplankton images
- 2015
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In: Machine Vision and Applications. - Heidelberg : Springer Berlin/Heidelberg. - 0932-8092 .- 1432-1769. ; 26:2-3, s. 305-315
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Journal article (peer-reviewed)abstract
- Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (λ-MART) helped to extract precise contour for 87.6 % of cells tested.
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| 9. |
- Gelzinis, Adas, et al.
(author)
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Automatic detection and morphological delineation of bacteriophages in electron microscopy images
- 2015
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In: Computers in Biology and Medicine. - Kidlington : Pergamon Press. - 0010-4825 .- 1879-0534. ; 64, s. 101-116
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Journal article (peer-reviewed)abstract
- Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.
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| 10. |
- Gelzinis, Adas, et al.
(author)
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Boosting performance of the edge-based active contour model applied to phytoplankton images
- 2012
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In: Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics. - Piscataway, NJ : IEEE Press. - 9781467352062 - 9781467352055 - 9781467352109 ; , s. 273-277
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Conference paper (peer-reviewed)abstract
- Automated contour detection for objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the core goal of this study. The speciesis known to cause harmful blooms in many estuarine and coastal environments. Active contour model (ACM)-based image segmentation is the approach adopted here as a potential solution. Currently, the main research in ACM area is highly focused ondevelopment of various energy functions having some physical intuition. This work, by contrast, advocates the idea of rich and diverse image preprocessing before segmentation. Advantage of the proposed preprocessing is demonstrated experimentally by comparing it to the six well known active contour techniques applied to the cell segmentation in microscopy imagery task. © 2012 IEEE.
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