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Improving medical decision making by self organizing intelligent systems
Peter Kokol, Petra Povalej, Gregor Štiglic, Dejan Dinevski, 2008, objavljeni znanstveni prispevek na konferenci

Ključne besede: cellular automata, classification, machine learning
Objavljeno: 05.06.2012; Ogledov: 968; Prenosov: 6
URL Polno besedilo (0,00 KB)

Developing a question answering system for the slovene language
Ines Čeh, Milan Ojsteršek, 2009, izvirni znanstveni članek

Opis: In todayćs world the majority of information is sought after on the internet. A common method is the use of search engines. However since the result of a query to the search engine is a ranked list of results, this is not the final step. It is up to the user to review the results and determine which of the results provides the information needed. Often this process is time consuming and does not provide the sought after information. Besides the number of returned results the limiting factor is often the lack of ability of the usersto form the correct query. The solution for this can be found in the formof question answering systems, where the user proposes a question in the natural language, similarly as talking to another person. The answer is the exact answer instead of a list of possible results. This paper presents the design of a question answering system in natural slovene language. The system searches for the answers for our target domain (Faculty of Electrical Engineering and Computer Science) with the use of a local database, databases of the facultyćs information system, MS Excel files and through web service calls. We have developed two separate applications: one for users and the other for the administrators of the system. With the help of the latter application the administrators supervise the functioning and use of entire system. The former application is actually the system that answers the questions.
Ključne besede: question answering, Slovenian language, question classification, machine learning, question templates, personalization
Objavljeno: 31.05.2012; Ogledov: 948; Prenosov: 6
URL Polno besedilo (0,00 KB)

Proceedings of the 23rd IEEE International Symposium on Computer-Based Medical Systems CBMS 2010, October 12-15, 2010 Perth, Australia
zbornik recenziranih znanstvenih prispevkov na mednarodni ali tuji konferenci

Ključne besede: medical systems, computer-based medical systems, machine learning methods, bioinformatics
Objavljeno: 05.06.2012; Ogledov: 609; Prenosov: 0
URL Polno besedilo (0,00 KB)

Comprehensive decision tree models in bioinformatics
Gregor Štiglic, Simon Kocbek, Igor Pernek, Peter Kokol, 2012, izvirni znanstveni članek

Opis: Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did notexpected significant differences in classification performance, the resultsdemonstrate a significant increase of accuracy in less complex visuallytuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumptionthat the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes anda high number of possibly redundant attributes that are very common in bioinformatics.
Ključne besede: decision tree models, machine learning technique, visual tuning, bioinformatics
Objavljeno: 05.06.2012; Ogledov: 668; Prenosov: 3
.pdf Polno besedilo (524,39 KB)

VTJ48 - Visually Tuned J48
Gregor Štiglic, 2012, programska oprema

Ključne besede: decision trees, comprehensible classifiers, machine learning
Objavljeno: 10.07.2015; Ogledov: 512; Prenosov: 3
URL Polno besedilo (0,00 KB)

A biometric authentication model using hand gesture images
Simon Fong, Yan Zhuang, Iztok Fister, Iztok Fister, 2013, izvirni znanstveni članek

Opis: A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information,associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i', 'l', 'o', 'v', 'e', and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. Itis believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy ofthis novel biometric authentication model which shows up to 93.75% recognition accuracy.
Ključne besede: biometric authentication, hand gesture, hand sign recognition, machine learning
Objavljeno: 28.06.2017; Ogledov: 40; Prenosov: 3
.pdf Polno besedilo (1,83 MB)

Link prediction in multiplex online social networks
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, Matjaž Perc, 2017, izvirni znanstveni članek

Opis: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Ključne besede: social networks, complex networks, signed networks, link prediction, machine learning
Objavljeno: 08.08.2017; Ogledov: 56; Prenosov: 1
.pdf Polno besedilo (940,17 KB)

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