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Your search keyword '"Iltanen, Kati"' showing total 3 results
Start Over Author "Iltanen, Kati" Topic artificial intelligence
3 results on '"Iltanen, Kati"'

1. Machine learning approach to an otoneurological classification problem.

Author

Joutsijoki H, Varpa K, Iltanen K, and Juhola M

Subjects
Algorithms, Bayes Theorem, Cluster Analysis, Humans, Models, Statistical, Regression Analysis, Reproducibility of Results, Support Vector Machine, Artificial Intelligence, Nervous System Diseases diagnosis, Vertigo diagnosis
Abstract

In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-Nearest Neighbour (k-NN) method and Naïve Bayes (NB) methods. Furthermore, Multinomial Logistic Regression (MNLR) was tested for the dataset. HAH-SVM with the linear kernel achieved clearly the best accuracy being 76.9% which was a good result with the dataset tested. From the other classification methods HAH-k-NN with cityblock metric, HAH-NB and MNLR methods achieved above 60% accuracy. Around 77% accuracy is a good result compared to previous researches with the same dataset.

Published
2013
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2. Machine learning method for knowledge discovery experimented with otoneurological data.

Author

Varpa K, Iltanen K, and Juhola M

Subjects
Humans, Artificial Intelligence, Decision Support Systems, Clinical, Diagnosis, Computer-Assisted methods, Diagnostic Techniques, Neurological, Diagnostic Techniques, Otological, Evidence-Based Medicine methods, Medical Records Systems, Computerized, Vertigo diagnosis
Abstract

We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. In this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as patterns of classes. Each pattern includes attributes with weight and fitness values concerning the class. With the knowledge discovery method it is possible to form fitness values from data. Knowledge formation is based on frequency distributions of attributes. Knowledge formed by the knowledge discovery method is tested with two vertigo data sets and compared to experts' knowledge. The experts' and machine learnt knowledge are also combined in various ways in order to examine effects of weights on classification accuracy. The classification accuracy of knowledge discovery method is compared to 1- and 5-nearest neighbour method and Naive-Bayes classifier. The results showed that knowledge bases combining machine learnt knowledge with the experts' knowledge yielded the best classification accuracies. Further, attribute weighting had an important effect on the classification capability of the system. When considering different diseases in the used data sets, the performance of the knowledge discovery method and the inference method is comparable to other methods employed in this study.

Published
2008
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3. Evaluation and classification of otoneurological data with new data analysis methods based on machine learning

Author

Siermala, Markku, Juhola, Martti, Laurikkala, Jorma, Iltanen, Kati, Kentala, Erna, and Pyykkö, Ilmari

Subjects
*ARTIFICIAL intelligence, *EDUCATIONAL evaluation, *ARTIFICIAL neural networks, *BIONICS
Abstract

Abstract: We improved the classification ability of multilayer perceptron networks by constructing a set of networks of as many as output classes and investigated the influence of different input variables on the classification. We have developed methods named scattering, spectrum and response analysis to express the classification complexity, especially the overlap of output classes, to disentangle the relation between the input variables and output classes of perceptron neural networks, and to establish the importance of input variables. The methods were tested by exploring complicated otoneurological data. In contrast to the variable selection problem, our methods characterize the importance of variables for classification and also describe the importance of the different values of each variable for output (disease) classes. When complex data is distributed in a biased manner between disease classes, we improved classification accuracy by developing a network set called NetSet, which increased average sensitivity and positive predictive value for at least 10% up to 85% and 83% respectively, compared to our earlier neural network classifications with the same data, which clarified class distribution effects and supported our comprehension of the significance of input. [Copyright &y& Elsevier]

Published
2007
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