| 1. |
- Håkansson, Maria, et al.
(author)
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Facilitating Mobile Music Sharing and Social Interaction with Push!Music
- 2007
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In: Proceedings of the 40th Hawaii International Conference on System Sciences. - Los Alamitos, Calif. : IEEE Computer Society Washington. - 1530-1605. - 0769527558 ; , s. 87-
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Conference paper (peer-reviewed)abstract
- Push!Music is a novel mobile music listening and sharing system, where users automatically receive songs that have autonomously recommended themselves from nearby players depending on similar listening behaviour and music history. Push!Music also enables users to wirelessly send songs between each other as personal recommendations. We conducted a two-week preliminary user study of Push!Music, where a group of five friends used the application in their everyday life. We learned for example that the shared music in Push!Music became a start for social interaction and that received songs in general were highly appreciated and could be looked upon as 'treats'.
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| 2. |
- Sutinen, Martti, et al.
(author)
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Web-Based Analytical Decision Support System
- 2010
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In: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. - : IEEE conference proceedings. - 9781424481347 - 9781424481354 ; , s. 575-579
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Conference paper (peer-reviewed)abstract
- This paper presents a web-application supporting structured decision modelling and analysis. The application allows for decision modelling with respect to different preferences and views, allowing for numerically imprecise and vague background probabilities, values, and criteria weights, which further can be adjusted in an interactive fashion when considering calculated decision outcomes. The web-application is based on a decision tool that has been used in a large number of different domains over the last 15 years, ranging from investment decision analysis for companies to public decision support for local governments.
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| 3. |
- Täckström, Oscar, et al.
(author)
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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
- 2010
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In: CoNLL 2010. - : Association for Computational Linguistics. ; , s. 84-91
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Conference paper (peer-reviewed)abstract
- This paper reports experiments for the CoNLL 2010 shared task on learning to detect hedges and their scope in natural language text. We have addressed the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection in the biological domain we use an L1-regularised binary support vector machine, while for sentence level weasel detection in the Wikipedia domain, we use an L2-regularised approach. We model the in-sentence uncertainty cue and scope detection task as an L2-regularised approximate maximum margin sequence labelling problem, using the BIO-encoding. In addition to surface level features, we use a variety of linguistic features based on a functional dependency analysis. A greedy forward selection strategy is used in exploring the large set of potential features. Our official results for Task 1 for the biological domain are 85.2 F1-score, for the Wikipedia set 55.4 F1-score. For Task 2, our official results are 2.1 for the entire task with a score of 62.5 for cue detection. After resolving errors and final bugs, our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes: 39.6 and cues: 78.5.
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| 4. |
- Laskowski, Kornel, 1972-, et al.
(author)
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On the dynamics of overlap in multi-party conversation
- 2012
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In: INTERSPEECH 2012. - Portland, USA : Curran Associates, Inc. - 9781622767595 ; , s. 846-849
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Conference paper (peer-reviewed)abstract
- Overlap, although short in duration, occurs frequently in multi- party conversation. We show that its duration is approximately log-normal, and inversely proportional to the number of simul- taneously speaking parties. Using a simple model, we demon- strate that simultaneous talk tends to end simultaneously less frequently than in begins simultaneously, leading to an arrow of time in chronograms constructed from speech activity alone. The asymmetry is significant and discriminative. It appears to be due to dialog acts which do not carry propositional content, and those which are not brought to completion.
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| 5. |
- Alfalahi, Alyaa, et al.
(author)
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Expanding a dictionary of marker words for uncertainty and negation using distributional semantics
- 2015
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In: EMNLP 2015 - 6th International Workshop on Health Text Mining and Information Analysis, LOUHI 2015 : Proceedings of the Workshop - Proceedings of the Workshop. - : Association for Computational Linguistics. - 9781941643327 ; , s. 90-96
-
Conference paper (peer-reviewed)abstract
- Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word.
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| 6. |
- Johansson, Ulf, et al.
(author)
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Conformal Prediction Using Decision Trees
- 2013
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In: IEEE 13th International Conference on Data Mining (ICDM). - : IEEE Computer Society. - 9780769551081 ; , s. 330-339
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Conference paper (peer-reviewed)abstract
- Conformal prediction is a relatively new framework in which the predictive models output sets of predictions with a bound on the error rate, i.e., in a classification context, the probability of excluding the correct class label is lower than a predefined significance level. An investigation of the use of decision trees within the conformal prediction framework is presented, with the overall purpose to determine the effect of different algorithmic choices, including split criterion, pruning scheme and way to calculate the probability estimates. Since the error rate is bounded by the framework, the most important property of conformal predictors is efficiency, which concerns minimizing the number of elements in the output prediction sets. Results from one of the largest empirical investigations to date within the conformal prediction framework are presented, showing that in order to optimize efficiency, the decision trees should be induced using no pruning and with smoothed probability estimates. The choice of split criterion to use for the actual induction of the trees did not turn out to have any major impact on the efficiency. Finally, the experimentation also showed that when using decision trees, standard inductive conformal prediction was as efficient as the recently suggested method cross-conformal prediction. This is an encouraging results since cross-conformal prediction uses several decision trees, thus sacrificing the interpretability of a single decision tree.
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| 7. |
- Johansson, Ulf, et al.
(author)
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Evolved Decision Trees as Conformal Predictors
- 2013
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In: 2013 IEEE Congress on Evolutionary Computation (CEC). - : IEEE. - 9781479904532 ; , s. 1794-1801
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Conference paper (peer-reviewed)abstract
- In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.
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| 8. |
- Johansson, Ulf, et al.
(author)
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Extending Nearest Neighbor Classification with Spheres of Confidence
- 2008
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In: Proceedings of the Twenty-First International FLAIRS Conference (FLAIRS 2008). - : AAAI Press. - 9781577353652 ; , s. 282-287
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Conference paper (peer-reviewed)abstract
- The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i.e., the number of neighbors, and the use of k as a global constant that is independent of the particular region in which theexample to be classified falls. Methods using weighted voting schemes only partly alleviate these problems, since they still involve choosing a fixed k. In this paper, a novel instance-based learner is introduced that does not require kas a parameter, but instead employs a flexible strategy for determining the number of neighbors to consider for the specific example to be classified, hence using a local instead of global k. A number of variants of the algorithm are evaluated on 18 datasets from the UCI repository. The novel algorithm in its basic form is shown to significantly outperform standard kNN with respect to accuracy, and an adapted version of the algorithm is shown to be clearlyahead with respect to the area under ROC curve. Similar to standard kNN, the novel algorithm still allows for various extensions, such as weighted voting and axes scaling.
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| 9. |
- Johansson, Ulf, et al.
(author)
-
Overproduce-and-Select : The Grim Reality
- 2013
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In: 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL). - : IEEE. - 9781467358538 ; , s. 52-59
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Conference paper (peer-reviewed)abstract
- Overproduce-and-select (OPAS) is a frequently used paradigm for building ensembles. In static OPAS, a large number of base classifiers are trained, before a subset of the available models is selected to be combined into the final ensemble. In general, the selected classifiers are supposed to be accurate and diverse for the OPAS strategy to result in highly accurate ensembles, but exactly how this is enforced in the selection process is not obvious. Most often, either individual models or ensembles are evaluated, using some performance metric, on available and labeled data. Naturally, the underlying assumption is that an observed advantage for the models (or the resulting ensemble) will carry over to test data. In the experimental study, a typical static OPAS scenario, using a pool of artificial neural networks and a number of very natural and frequently used performance measures, is evaluated on 22 publicly available data sets. The discouraging result is that although a fairly large proportion of the ensembles obtained higher test set accuracies, compared to using the entire pool as the ensemble, none of the selection criteria could be used to identify these highly accurate ensembles. Despite only investigating a specific scenario, we argue that the settings used are typical for static OPAS, thus making the results general enough to question the entire paradigm.
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| 10. |
- Johansson, Ulf, et al.
(author)
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Random Brains
- 2013
-
In: The 2013 International Joint Conference on Neural Networks (IJCNN). - : IEEE. - 9781467361286 ; , s. 1-8
-
Conference paper (peer-reviewed)abstract
- In this paper, we introduce and evaluate a novel method, called random brains, for producing neural network ensembles. The suggested method, which is heavily inspired by the random forest technique, produces diversity implicitly by using bootstrap training and randomized architectures. More specifically, for each base classifier multilayer perceptron, a number of randomly selected links between the input layer and the hidden layer are removed prior to training, thus resulting in potentially weaker but more diverse base classifiers. The experimental results on 20 UCI data sets show that random brains obtained significantly higher accuracy and AUC, compared to standard bagging of similar neural networks not utilizing randomized architectures. The analysis shows that the main reason for the increased ensemble performance is the ability to produce effective diversity, as indicated by the increase in the difficulty diversity measure.
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