| 1. |
- Alzghoul, Ahmad, et al.
(författare)
-
Screening paper runnability in a web-offset pressroom by data mining
- 2009
-
Ingår i: Proceedings of the 9th Industrial Conference on Advances in Data Mining. - Berlin : Springer Berlin/Heidelberg. - 9783642030666 ; , s. 161-175
-
Konferensbidrag (refereegranskat)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%.
|
|
| 2. |
- Bacauskiene, Marija, et al.
(författare)
-
A feature selection technique for generation of classification committees and its application to categorization of laryngeal images
- 2009
-
Ingår i: Pattern Recognition. - New York : Pergamon Press. - 0031-3203 .- 1873-5142. ; 42:5, s. 645-654
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 3. |
|
|
| 4. |
- Bacauskiene, Marija, et al.
(författare)
-
Random forests based monitoring of human larynx using questionnaire data
- 2012
-
Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 5506-5512
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 5. |
- Bacauskiene, Marija, et al.
(författare)
-
Selecting salient features for classification based on neural network committees
- 2004
-
Ingår i: Pattern Recognition Letters. - Amsterdam : Elsevier Science. - 0167-8655 .- 1872-7344. ; 25:16, s. 1879-1891
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 6. |
- Bacauskiene, Marija, et al.
(författare)
-
Selecting variables for neural network committees
- 2006
-
Ingår i: Advances in neural networks - ISNN 2006. - Berlin : Springer Berlin/Heidelberg. - 9783540344391 ; , s. 837-842
-
Konferensbidrag (refereegranskat)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.
|
|
| 7. |
- Bacauskiene, Marija, et al.
(författare)
-
The Evidence Theory Based Post-Processing of Colour Images
- 2004
-
Ingår i: Informatica (Vilnius). - Vilnius : Institute of Mathematics and Cybernetics, Lithuanian Academy of Sciences. - 0868-4952 .- 1822-8844. ; 15:3, s. 315-328
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 8. |
- Bergman, Linda, et al.
(författare)
-
Intelligent Monitoring of the Offset Printing Process
- 2004
-
Ingår i: Neural Networks and Computational Intelligence - Proceedings. - : ACTA Press. ; , s. 173-178, s. 173-178
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we present a neural networks and image analysis based approach to assessing colour deviations in an offset printing process from direct measurements on halftone multicoloured pictures--there are no measuring areas printed solely to assess the deviations. A committee of neural networks is trained to assess the ink proportions in a small image area. From only one measurement the trained committee is capable of estimating the actual amount of printing inks dispersed on paper in the measuring area. To match the measured image area of the printed picture with the corresponding area of the original image, when comparing the actual ink proportions with the targeted ones, properties of the 2-D Fourier transform are exploited.
|
|
| 9. |
- Bergman, Lars, et al.
(författare)
-
Modelling and Control of the Web-Fed Offset Newspaper Printing Press
- 2003
-
Ingår i: Proceedings of the Technical Association of the Graphic Arts, TAGA. - : Technical Association of the Graphic (TAGA). ; , s. 27-29
-
Konferensbidrag (refereegranskat)abstract
- We present an approach to modelling and controlling the web-fed offset printing process. An image processing and artificial neural networks based device is used to measure the printing process output - the observable variables. The observable variables are measured on halftone areas and integrate information about both ink densities and dot sizes. From only one measurement the device is capable of estimating the actual relative amount of each cyan, magenta, yellow, and black ink dispersed on paper in the measuring area. We build and test linear and non-linear printing press models using the measured variables andother parameters characterising the press. The observable variables measured and the press model developed are then further used by a control unit for generating control signals - signals for controlling the ink keys - to compensate for colour deviation. The experimental investigations performed have shown that the non-linear model developed is accurate enough to be used in a control loop for controlling the printing process. The control accuracy - the tracking accuracy of the desired ink level - obtained from the controller was higher than that observed when controlling the press by the operator.
|
|
| 10. |
- Bergman, Lars, et al.
(författare)
-
Unsupervised colour image segmentation applied to printing quality assessment
- 2005
-
Ingår i: Image and Vision Computing. - Amsterdam : Elsevier. - 0262-8856 .- 1872-8138. ; 23:4, s. 417-425
-
Tidskriftsartikel (refereegranskat)abstract
- We present an option for colour image segmentation applied to printing quality assessment in offset lithographic printing by measuring an average ink dot size in halftone pictures. The segmentation is accomplished in two stages through classification of image pixels. In the first stage, rough image segmentation is performed. The results of the first segmentation stage are then utilized to collect a balanced training data set for learning refined parameters of the decision rules. The developed software is successfully used in a printing shop to assess the ink dot size on paper and printing plates.
|
|
