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  • 51.
    Linusson, Henrik
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Norinder, Ulf
    Swetox, Karolinska Institutet.
    Boström, Henrik
    Dept. of Computer Science and Informatics, Stockholm University.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Löfström, Tuve
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    On the Calibration of Aggregated Conformal Predictors2017In: Proceedings of Machine Learning Research, 2017Conference paper (Refereed)
    Abstract [en]

    Conformal prediction is a learning framework that produces models that associate witheach of their predictions a measure of statistically valid confidence. These models are typi-cally constructed on top of traditional machine learning algorithms. An important result ofconformal prediction theory is that the models produced are provably valid under relativelyweak assumptions—in particular, their validity is independent of the specific underlyinglearning algorithm on which they are based. Since validity is automatic, much research onconformal predictors has been focused on improving their informational and computationalefficiency. As part of the efforts in constructing efficient conformal predictors, aggregatedconformal predictors were developed, drawing inspiration from the field of classification andregression ensembles. Unlike early definitions of conformal prediction procedures, the va-lidity of aggregated conformal predictors is not fully understood—while it has been shownthat they might attain empirical exact validity under certain circumstances, their theo-retical validity is conditional on additional assumptions that require further clarification.In this paper, we show why validity is not automatic for aggregated conformal predictors,and provide a revised definition of aggregated conformal predictors that gains approximatevalidity conditional on properties of the underlying learning algorithm.

  • 52.
    Löfström, Tuve
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Boström, Henrik
    Stockholm University, Department of Computer and Systems Sciences.
    Linusson, Henrik
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Bias Reduction through Conditional Conformal Prediction2015In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 6, p. 1355-1375Article in journal (Refereed)
  • 53.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Borström, Henrik
    Ensemble Member Selection Using Multi-Objective Optimization2009Conference paper (Refereed)
    Abstract [en]

    Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy on novel data. Several studies have, however, shown that it is difficult to come up with a single measure, such as ensemble or base classifier selection set accuracy, or some measure based on diversity, that is a good general predictor for ensemble test accuracy. This paper presents a novel technique that for each learning task searches for the most effective combination of given atomic measures, by means of a genetic algorithm. Ensembles built from either neural networks or random forests were empirically evaluated on 30 UCI datasets. The experimental results show that when using the generated combined optimization criteria to rank candidate ensembles, a higher test set accuracy for the top ranked ensemble was achieved, compared to using ensemble accuracy on selection data alone. Furthermore, when creating ensembles from a pool of neural networks, the use of the generated combined criteria was shown to generally outperform the use of estimated ensemble accuracy as the single optimization criterion.

  • 54.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks2010Conference paper (Refereed)
    Abstract [en]

    It is well-known that ensemble performance relies heavily on sufficient diversity among the base classifiers. With this in mind, the strategy used to balance diversity and base classifier accuracy must be considered a key component of any ensemble algorithm. This study evaluates the predictive performance of neural network ensembles, specifically comparing straightforward techniques to more sophisticated. In particular, the sophisticated methods GASEN and NegBagg are compared to more straightforward methods, where each ensemble member is trained independently of the others. In the experimentation, using 31 publicly available data sets, the straightforward methods clearly outperformed the sophisticated methods, thus questioning the use of the more complex algorithms.

  • 55.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    University of Borås, School of Business and IT.
    Effective Utilization of Data in Inductive Conformal Prediction2013Conference paper (Refereed)
    Abstract [en]

    Conformal prediction is a new framework producing region predictions with a guaranteed error rate. Inductive conformal prediction (ICP) was designed to significantly reduce the computational cost associated with the original transductive online approach. The drawback of inductive conformal prediction is that it is not possible to use all data for training, since it sets aside some data as a separate calibration set. Recently, cross-conformal prediction (CCP) and bootstrap conformal prediction (BCP) were proposed to overcome that drawback of inductive conformal prediction. Unfortunately, CCP and BCP both need to build several models for the calibration, making them less attractive. In this study, focusing on bagged neural network ensembles as conformal predictors, ICP, CCP and BCP are compared to the very straightforward and cost-effective method of using the out-of-bag estimates for the necessary calibration. Experiments on 34 publicly available data sets conclusively show that the use of out-of-bag estimates produced the most efficient conformal predictors, making it the obvious preferred choice for ensembles in the conformal prediction framework.

  • 56.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers2008Conference paper (Refereed)
    Abstract [en]

    The test set accuracy for ensembles of classifiers selected based on single measures of accuracy and diversity as well as combinations of such measures is investigated. It is found that by combining measures, a higher test set accuracy may be obtained than by using any single accuracy or diversity measure. It is further investigated whether a multi-criteria search for an ensemble that maximizes both accuracy and diversity leads to more accurate ensembles than by optimizing a single criterion. The results indicate that it might be more beneficial to search for ensembles that are both accurate and diverse. Furthermore, the results show that diversity measures could compete with accuracy measures as selection criterion.

  • 57.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    The Problem with Ranking Ensembles Based on Training or Validation Performance2008In: Proceedings of the International Joint Conference on Neural Networks, IEEE Press , 2008Conference paper (Refereed)
    Abstract [en]

    The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets, shows that there is in general nothing to gain in performance on novel data by choosing an ensemble based on any of the training measures evaluated here. This is despite the fact that the measures evaluated include all the most frequently used; i.e. ensemble training and validation accuracy, base classifier training and validation accuracy, ensemble training and validation AUC and two diversity measures. The main reason is that all ensembles tend to have quite similar performance, unless we deliberately lower the accuracy of the base classifiers. The key consequence is, of course, that a data miner can do no better than picking an ensemble at random. In addition, the results indicate that it is futile to look for an algorithm aimed at optimizing ensemble performance by somehow selecting a subset of available base classifiers.

  • 58.
    Löfström, Tuve
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    Using Optimized Optimization Criteria in Ensemble Member Selection2009Conference paper (Refereed)
    Abstract [en]

    Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. This paper presents a novel technique, where genetic algorithms are used for combining several measurements into a complex criterion that is optimized separately for each dataset. The experimental results show that when using the generated combined optimization criteria to rank candidate ensembles, a higher test set accuracy for the top ranked ensemble was achieved compared to using other measures alone, e.g., estimated ensemble accuracy or the diversity measure difficulty.

  • 59.
    Löfström, Tuve
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Linnusson, Henrik
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Sönströd, Cecilia
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    System Health Monitoring using Conformal Anomaly Detection2015Report (Other (popular science, discussion, etc.))
  • 60.
    Löfström, Tuwe
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Balkow, Jenny
    Sundell, Håkan
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    A data-driven approach to online fitting services2018In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium), 2018, p. 1559-1566Conference paper (Refereed)
    Abstract [en]

    Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5% of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8%.

  • 61.
    Reveiro, Maria
    et al.
    Högskolan i Skövde.
    Dahlbom, Anders
    Högskolan i Skövde.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Brattberg, Peter
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems2015In: Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Panayiotis Zaphiris, Andri Ioannou, Los Angeles, 2015, Vol. 9192, p. 279-290Conference paper (Refereed)
    Abstract [en]

    Golf is a popular sport around the world. Since an accomplished golf swing is essential for succeeding in this sport, golf players spend a considerable amount of time perfecting their swing. In order to guide the design of future computer-based training systems that support swing instruction, this paper analyzes the data gathered during interviews with golf instructors and participant observations of actual swing coaching sessions. Based on our field work, we describe the characteristics of a proficient swing, how the instructional sessions are normally carried out and the challenges professional instructors face. Taking into account these challenges, we outline which desirable capabilities future computer-based training systems for professional golf instructors should have.

  • 62.
    Rikard, König
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Ulf, Johansson
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Lindqvist, Ann
    Scania CV AB.
    Peter, Brattberg
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Interesting Regression- and Model Trees Through Variable Restrictions2015In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, p. 281-292Conference paper (Refereed)
    Abstract [en]

    The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5’s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evalu ated against M5’s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.

  • 63.
    Sundell, Håkan
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    König, Rikard
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Pragmatic Approach to Association Rule Learning in Real-World Scenarios2015Conference paper (Refereed)
  • 64.
    Sundell, Håkan
    et al.
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Löfström, Tuve
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Johansson, Ulf
    University of Borås, Faculty of Librarianship, Information, Education and IT.
    Explorative multi-objective optimization of marketing campaigns for the fashion retail industry2018In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez and Etienne E Kerre, 2018, p. 1551-1558Conference paper (Refereed)
    Abstract [en]

    We show how an exploratory tool for association rule mining can be used for efficient multi-objective optimization of marketing campaigns for companies within the fashion retail industry. We have earlier designed and implemented a novel digital tool for mining of association rules from given basket data. The tool supports efficient finding of frequent itemsets over multiple hierarchies and interactive visualization of corresponding association rules together with numerical attributes. Normally when optimizing a marketing campaign, factors that cause an increased level of activation among the recipients could in fact reduce the profit, i.e., these factors need to be balanced, rather than optimized individually. Using the tool we can identify important factors that influence the search for an optimal campaign in respect to both activation and profit. We show empirical results from a real-world case-study using campaign data from a well-established company within the fashion retail industry, demonstrating how activation and profit can be simultaneously targeted, using computer-generated algorithms as well as human-controlled visualization.

  • 65.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Concept Description: A Fresh Look2007In: The International Joint Conference on Neural Networks, IEEE Press , 2007, p. 2415-2420Chapter in book (Other academic)
  • 66.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Towards a Unified View on Concept Description2007Conference paper (Refereed)
  • 67.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Boström, Henrik
    Norinder, Ulf
    Pin-Pointing Concept Descriptions2010Conference paper (Refereed)
    Abstract [en]

    In this study, the task of obtaining accurate and comprehensible concept descriptions of a specific set of production instances has been investigated. The suggested method, inspired by rule extraction and transductive learning, uses a highly accurate opaque model, called an oracle, to coach construction of transparent decision list models. The decision list algorithms evaluated are JRip and four different variants of Chipper, a technique specifically developed for concept description. Using 40 real-world data sets from the drug discovery domain, the results show that employing an oracle coach to label the production data resulted in significantly more accurate and smaller models for almost all techniques. Furthermore, augmenting normal training data with production data labeled by the oracle also led to significant increases in predictive performance, but with a slight increase in model size. Of the techniques evaluated, normal Chipper optimizing FOIL’s information gain and allowing conjunctive rules was clearly the best. The overall conclusion is that oracle coaching works very well for concept description.

  • 68.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Evolving Accurate and Comprehensible Classification Rules2011Conference paper (Refereed)
    Abstract [en]

    In this paper, Genetic Programming is used to evolve ordered rule sets (also called decision lists) for a number of benchmark classification problems, with evaluation of both predictive performance and comprehensibility. The main purpose is to compare this approach to the standard decision list algorithm JRip and also to evaluate the use of different length penalties and fitness functions for evolving this type of model. The results, using 25 data sets from the UCI repository, show that genetic decision lists with accuracy-based fitness functions outperform JRip regarding accuracy. Indeed, the best setup was significantly better than JRip. JRip, however, held a slight advantage over these models when evaluating AUC. Furthermore, all genetic decision list setups produced models that were more compact than JRip models, and thus more readily comprehensible. The effect of using different fitness functions was very clear; in essence, models performed best on the evaluation criterion that was used in the fitness function, with a worsening of the performance for other criteria. Brier score fitness provided a middle ground, with acceptable performance on both accuracy and AUC. The main conclusion is that genetic programming solves the task of evolving decision lists very well, but that different length penalties and fitness functions have immediate effects on the results. Thus, these parameters can be used to control the trade-off between different aspects of predictive performance and comprehensibility.

  • 69.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    König, Rikard
    University of Borås, School of Business and IT.
    Niklasson, Lars
    Genetic Decision Lists for Concept Description2008In: Proceeding of The 2008 International Conference on Data Mining, CSREA Press , 2008, p. 450-457Conference paper (Refereed)
  • 70.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Löfström, Tuve
    University of Borås, School of Business and IT.
    Evaluating Algorithms for Concept Description2009Conference paper (Refereed)
    Abstract [en]

    When performing concept description, models need to be evaluated both on accuracy and comprehensibility. A comprehensible concept description model should present the most important relationships in the data in an accurate and understandable way. Two natural representations for this are decision trees and decision lists. In this study, the two decision list algorithms RIPPER and Chipper, and the decision tree algorithm C4.5, are evaluated for concept description, using publicly available datasets. The experiments show that C4.5 performs very well regarding accuracy and brevity, i.e. the ability to classify instances with few tests, but also produces large models that are hard to survey and contain many extremely specific rules, thus not being good concept descriptions. The decision list algorithms perform reasonably well on accuracy, and are mostly able to produce small models with relatively good predictive performance. Regarding brevity, Chipper is better than RIPPER, using on average fewer conditions to classify an instance. RIPPER, on the other hand, excels in relevance, i.e. the ability to capture a large number of instances with every rule.

  • 71.
    Sönströd, Cecilia
    et al.
    University of Borås, School of Business and IT.
    Johansson, Ulf
    University of Borås, School of Business and IT.
    Norinder, Ulf
    Generating Comprehensible QSAR Models2009Conference paper (Refereed)
    Abstract [en]

    This paper presents work in progress from the INFUSIS project and contains initial experimentation, using publicly available medicinal chemistry datasets, on obtaining comprehensible QSAR models. Three techniques are evaluated on both predictive performance, measured as accuracy, and comprehensibility, measured as model size. The chosen techniques are J48 decision trees and JRip and Chipper decision lists. The results show that J48 obtains superior accuracy and that Chipper performs best of the two decision list algorithms on accuracy. Furthermore, it is seen that, regarding accuracy, all techniques benefit from feature reduction, which almost always results in increased accuracy. Regarding comprehensibility, JRip obtains the smallest models, followed by Chipper, with J48 producing the largest models. For model size, feature reduction is not seen to be universally beneficial; only J48 produces smaller models for the reduced datasets, while both decision list algorithms actually produce larger models on average. The overall conclusion is that, for these datasets, there exists a definite tradeoff between accuracy and comprehensibility that needs to be investigated further.

12 51 - 71 of 71
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