his.sePublications
Change search
Refine search result
12 51 - 74 of 74
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 51. Johansson, Ulf
    et al.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics.
    König, Rikard
    Accuracy vs. comprehensibility in data mining models2004In: Proceedings of the Seventh International Conference on Information Fusion: 28 June - 1 July 2004 Stockholm Sweden, 2004, p. 295-300Conference paper (Other academic)
    Abstract [en]

    This paper addresses the important issue of the tradeoff between accuracy and comprehensibility in data mining. The paper presents results which show that it is, to some extent, possible to bridge this gap. A method for rule extraction from opaque models (Genetic Rule EXtraction – G-REX) is used to show the effects on accuracy when forcing the creation of comprehensible representations. In addition the technique of combining different classifiers to an ensemble is demonstrated on some well-known data sets. The results show that ensembles generally have very high accuracy, thus making them a good first choice when performing predictive data mining.

  • 52.
    Johansson, Ulf
    et al.
    University of Borås.
    Sönströd, Cecilia
    University of Borås.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Explaining winning poker: a data mining approach2006In: 24th Annual Workshop of the Swedish Artificial Intelligence, IEEE Computer Society, 2006, p. 129-134Conference paper (Refereed)
    Abstract [en]

    This paper presents an application where machine learning techniques are used to mine data gathered from online poker in order to explain what signifies successful play. The study focuses on short-handed small stakes Texas Hold'em, and the data set used contains 105 human players, each having played more than 500 hands. Techniques used are decision trees and G-REX, a rule extractor based on genetic programming. The overall result is that the rules induced are rather compact and have very high accuracy, thus providing good explanations of successful play. It is of course quite hard to assess the quality of the rules; i.e. if they provide something novel and non-trivial. The main picture is, however, that obtained rules are consistent with established poker theory. With this in mind, we believe that the suggested techniques will in future studies, where substantially more data is available, produce clear and accurate descriptions of what constitutes the difference between winning and losing in poker.

  • 53.
    Johansson, Ulf
    et al.
    Department of Business, University of Borås, Sweden.
    Sönströd, Cecilia
    Department of Business, University of Borås, Sweden.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics.
    Why Rule Extraction Matters2004In: Proceedings of the Eighth IASTED International Conference on Software Engineering and Applications, Cambridge, MA, United States, 9 November 2004 through 11 November 2004 / [ed] M. H. Hamza, 2004, p. 47-52Conference paper (Other academic)
    Abstract [en]

    The purpose of this paper is to argue for rule extraction as an integral part of data mining. The paper contains two case studies where rule extraction is used for typical data mining tasks. More specifically, rule extraction is used both to explain existing opaque models and to produce concept description via an opaque model. The main result is that the rule extraction approach generally yields comprehensible models with higher accuracy than transparent models created directly from the data set by CART and See5. The implication is that rule extraction can enhance the capability of data mining for decision support.

  • 54.
    König, Rikard
    et al.
    University of Borås.
    Johansson, Ulf
    University of Borås.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Genetic Programming: a Tool for Flexible Rule Extraction2007In: IEEE Congress on Evolutionary Computation (CEC, IEEE Press, 2007, p. 1304-1310Conference paper (Refereed)
    Abstract [en]

    Although data mining is performed to support decision making, many of the most powerful techniques, like neural networks and ensembles, produce opaque models. This lack of interpretability is an obvious disadvantage, since decision makers normally require some sort of explanation before taking action. To achieve comprehensibility, accuracy is often sacrificed by the use of simpler, transparent models, such as decision trees. Another alternative is rule extraction; i.e. to transform the opaque model into a comprehensible model, keeping acceptable accuracy. We have previously suggested a rule extraction algorithm named G-REX, which is based on genetic programming. One key property of G-REX, due to the use of genetic programming, is the possibility to use different representation languages. In this study we apply G-REX to estimation tasks. More specifically, three representation languages are evaluated using eight publicly available data sets. The quality of the extracted rules is compared to two standard techniques producing comprehensible models; multiple linear regression and the decision tree algorithm C&RT. The results show that G-REX outperforms the standard techniques, but that the choice of representation language is important.

  • 55.
    König, Rikard
    et al.
    University of Borås, Sweden.
    Johansson, Ulf
    University of Borås, Sweden.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    G-REX: A Versatile Framework for Evolutionary Data Mining2008In: Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, IEEE Computer Society, 2008, p. 971-974Conference paper (Refereed)
    Abstract [en]

     

    This paper presents G-REX, a versatile data mining framework based on Genetic Programming. What differs G-REX from other GP frameworks is that it doesn’t strive to be a general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a multitude of predefined classification and regression models. Examples of predefined models are decision trees, decision lists, k-NN with attribute weights, hybrid kNN-rules, fuzzy-rules and several different regression models. The main strength is, however, the flexibility, making it easy to modify, extend and combine all of the predefined functionality. G-REX is, in addition, available in a special Weka package adding useful evolutionary functionality to the standard data mining tool Weka.

     

     

  • 56.
    König, Rikard
    et al.
    University of Skövde, School of Humanities and Informatics.
    Johansson, Ulf
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics.
    Increasing rule extraction comprehensibility2006In: International Journal of Information Technology and Intelligent Computing, ISSN 1895-8648, Vol. 1, no 2, p. 303-314Article in journal (Refereed)
  • 57. König, Rikard
    et al.
    Johansson, Ulf
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Instance Ranking using Ensemble Spread2007In: The 2007 International Conference on Data Mining, DMIN'07, CSREA Press, 2007, p. 73-78Conference paper (Refereed)
    Abstract [en]

    This paper investigates a technique for predicting ensemble uncertainty originally proposed in the weather forecasting domain. The overall purpose is to find out if the technique can be modified to operate on a wider range of regression problems. The main difference, when moving outside the weather forecasting domain, is the lack of extensive statistical knowledge readily available for weather forecasting. In this study, three different modifications are suggested to the original technique. In the experiments, the modifications are compared to each other and to two straightforward technniques, using ten publicly available regression problems. Three of the techniques show promising result, especially one modification based on genetic algorithms. The suggested modification can accurately determine whether the confidence in ensemble predictions should be high or low.

  • 58. König, Rikard
    et al.
    Johansson, Ulf
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    The Importance of Representation Languages When Extracting Estimation Rules2007In: Proceedings of the 24th Annual Workshop of the Swedish Artificial Intelligence Society, University College of Borås , 2007, p. 136-146Conference paper (Refereed)
    Abstract [en]

    Data mining is performed to support decision making, but many of the most powerful techniques such as neural networks, or ensembles produce opaque models which are not comprehensible for a human. The lack of interpretability is an obvious disadvantage since decision makers require some sort of explanation before taking action. To achieve comprehensibility, accuracy is often sacrificed by the use of simpler models such as decision trees. Another alternative is, however, to extract rules from the opaque model. We have previously suggested a rule extration algorithm namned G-REX. In this study we further evaluate G-REX on estimation tasks. Two new representation languages are compared to the original, using eight publicly available datasets. The extracted rules are compared to two standard techniques producing comprehensible models; multiple linear regression and the decision tree algorith C&RT. The results show that G-REX outperforms the standard techniques when an appropriate representation is used.

  • 59.
    König, Rikard
    et al.
    School of Business and Informatics, University of Borås, Sweden.
    Johanssson, Ulf
    School of Business and Informatics, University of Borås, Sweden.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Using Genetic Programming to Increase Rule Quality2008In: Proceedings of the Twenty-First International FLAIRS Conference (FLAIRS 2008), AAAI Press, 2008, p. 288-293Conference paper (Refereed)
    Abstract [en]

    Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to the rules by increasing their AUC or brevity significantly.

  • 60. Löfström, Tuve
    et al.
    Johansson, UlfSönströd, CeciliaKönig, RikardNiklasson, LarsUniversity of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Proceedings of SAIS 2007: The 24th Annual Workshop of the Swedish Artificial Intelligence Society, Borås, May 22-23, 20072007Conference proceedings (editor) (Other academic)
  • 61.
    Löfström, Tuve
    et al.
    University of Skövde, School of Humanities and Informatics.
    König, Richard
    University of Skövde, School of Humanities and Informatics.
    Johansson, Ulf
    University of Skövde, School of Humanities and Informatics.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics.
    Strand, Mattias
    University of Skövde, School of Humanities and Informatics.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics.
    Benefits of Relating the Retail Domain to Information Fusion2006In: 9th International Conference on Information Fusion: IEEE ISIF, IEEE conference proceedings, 2006, p. Article number 4085930-Conference paper (Refereed)
  • 62.
    Niklasson, Lars
    University of Skövde, Department of Computer Science.
    Bounds on Test Effort for Event-Triggered Real-Time Systems1999Report (Other academic)
    Abstract [en]

    The test effort required for full test coverage is much higher in an event-triggered than in a time-triggered real-timesystem. This makes it difficult to attain confidence in the correctness of event-triggered real-time applications by testing,which is a necessary complement to other verification methods. We present a more general upper bound on the test effort of constrained event-triggered real-time systems, assuming multiple resources (a refinement of previous results). The emphasis is on system level testing of application timeliness, assuming that sufficient confidence in its functional correctness has been attained. Covered fault types include incorrect assumptions about temporal attributes of application and execution environment, and synchronization faults. An analysis of the effects that our constraints have on predictability and efficiency shows that the use of designated preemption points is required. A key factor in this approach is the ability to reduce the number of required test cases while maintaining full test coverage.

  • 63.
    Niklasson, Lars
    University of Skövde, Department of Computer Science.
    Structure Sensitivity in Connectionist Models1993Report (Other academic)
    Abstract [en]

    HS-IDA-TR-93-003. Annotation: Published in The Proceedings of the 1993 Connectionist Models Summer School, (Eds) Mozer et al., Lawrence Erlbaum, 1993.

  • 64.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Bodén, Mikael
    University of Skövde, Department of Computer Science. Department of CS and EE, University of Queensland, Australia.
    Content, Context and Connectionist Networks1999Report (Other academic)
    Abstract [en]

    The question whether connectionism offers a new way of looking at the cognitive architecture, or if its main contribution is as an implementational account of the classical (symbol) view, has been extensively debated for the last decade. Of special interest in this debate has been to achieve tasks which easily can be explained within the symbolic framework, i.e., tasks which seemingly require the possession of a systematicity of representation and process, in a novel way in connectionist systems.

    In this paper we argue that connectionism can offer a new explanational framework for aspects of cognition. Specifically, we argue that connectionism can offer new notions of compositionality, content and context-dependence based on connectionist primitives, i.e., architectures, learning, weights and internal activations, which open up for new variations of systematicity.

  • 65.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Bodén, Mikael
    University of Skövde, Department of Computer Science.
    Representing Structure and Structured Representations in Connectionist Networks1997Report (Other academic)
  • 66.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Engström, Henrik
    University of Skövde, Department of Computer Science.
    Johansson, Ulf
    Department of Business and Informatics, University of Borås, Sweden.
    An Adaptive 'Rock, Scissors and Paper' Player Based on a Tapped Delay Neural Network2001In: Proceedings of the International Conference on Application and Development of Computer Games in the 21st Century (ADCOG), 2001, p. 130-136Conference paper (Refereed)
    Abstract [en]

    This paper presents an adaptive 'rock, scissors and paper' artificial player. The artificial player is based on an adaptive neural network algorithm. The hypothesis is that human players do not adopt the optimal playing strategy, i.e. to use random moves, and that the artificial player could exploit this and adopt a winning strategy. To test this hypothesis a WAP-based and a web-based version of the artificial player was made available to the general public. A total of about 3000 human players have played against the artificial player, to date. Several different training strategies are evaluated, and the results show that efficient algorithms can be constructed. The best result being 72% won games for the artificial player and 28% won by human players. The paper also identifies future interesting issues for both game developers as well as researchers within Human Computer Interaction. 

  • 67.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Saab Microwave Systems, Skövde, Sweden.
    A Unified Situation Analysis Model for Human and Machine Situation Awareness2007In: INFORMATIK 2007: Informatik trifft Logistik: Band 2: Beiträge der 37. Jahrestagung der Gesellschaft für Informatik e.V. (GI) 24. - 27. September 2007 in Bremen / [ed] Otthein Herzog, Karl-Heinz Rödiger, Marc Ronthaler, Rainer Koschke, Bonn: Gesellschaft für Informatik , 2007, p. 105-109Conference paper (Refereed)
  • 68.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Extending the scope of Situation Analysis2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), Cologne, Germany, June 30–July 3, 2008, IEEE Press, 2008, p. 454-461Conference paper (Refereed)
    Abstract [en]

    The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

  • 69.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Sharkey, Noel E.
    University of Skövde, Department of Computer Science.
    Connectionism and the Issues of Compositionality and Systematicity1992Report (Other academic)
    Abstract [en]

    Connectionism as a model of the mind has been attacked by the advocators of the classical paradigm, who claim that Connectionism can only work if it is an implementation of Classical representations. This could be true for some of the models that claim to be Connectionist, but it will in this paper be shown that this is not true for Connectionist architectures that use non-symbolic representations. We will provide evidence in the form of simulation results that severely weaken of the arguments raised by Fodor and Pylyshyn and Fodor and McLaughlin, including their two main arguments, which are the lack of compositionality and systematicity.

  • 70.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Sharkey, Noel E.
    Centre for Connection Science, University of Exeter, UK.
    Connectionism: The Miracle Mind Model1992Report (Other academic)
    Abstract [en]

    Abstract: Connectionism as a model of the mind has recently been challenging the Classical model, in which the mind is regarded as symbol manipulating system. The main arguments against Connectionism concern its inability to form mental representations for complex expressions, which can be used for structure sensitive operations. Some argue for hybrid models which combine some of the most attractive features of the Classical and Connectionist models. This paper starts off by examining the definitions of the different approaches and also their strengths and weaknesses. One section is devoted to the debate between the advocators of the different paradigms, including the arguments about the lack of compositionality and systematicity in Connectionist cognitive models. We then argue for the Connectionist approach as the most attractive model of the mind. This includes performing the "miracle" of defining structure sensitive operations on non-symbolic representations of concepts.

  • 71.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Sharkey, Noel E.
    University of Sheffield, UK.
    Systematicity and Generalisation in Connectionist Compositional Representation1993Report (Other academic)
    Abstract [en]

    It has been argued that models, that are claimed to be models of the mind, have to exhibit a behaviour closely related to human thought. This includes dealing with the issues of compositionality, systematicity and productivity. This paper starts by describing a non-concatenative mode of combination for connectionist patterns of neural activation. We then turn to the issue of systematicity, i.e. structure sensitive processes. We explore this issue to some level of detail, e.g. the importance of choosing the `right' type of representation and how the construction of the training set could result in different types of systematicity.

  • 72.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    van Gelder, Tim
    Philosophy Program, Research School of Social Sciences, Australian National University, Canberra, Australia.
    Can Connectionist Models Exhibit Non-Classical Structure Sensitivity?1994Report (Other academic)
    Abstract [en]

    Several connectionist models have been supplying non-classical explanations to the challenge of explaining systematicity, i.e., structure sensitive processes, without merely being implementations of classical architectures. However, lately the challenge has been extended to include learning related issues. It has been claimed that when these issues are taken into account, only a restricted form of systematicity could be claimed by the connectionist models put forward so far. In this paper we investigate this issue further, and supply a model and results that satisfies even the revised challenge.

  • 73.
    Niklasson, Lars
    et al.
    University of Skövde, Department of Computer Science.
    Ziemke, Tom
    University of Skövde, Department of Computer Science.
    Lärande Datorer: Utopi eller Verklighet?1996Report (Other (popular science, discussion, etc.))
    Abstract [sv]

    Denna populärvetenskapliga rapport ger en kort introduktion till självlärande artificiella neurala nätverk, samt sätter dem i relation till den science fiction-version som ges på TV och film.

  • 74.
    Ziemke, Tom
    et al.
    University of Skövde, Department of Computer Science.
    Bodén, Mikael
    University of Skövde, Department of Computer Science.
    Niklasson, Lars
    University of Skövde, Department of Computer Science.
    Oil Spill Detection: A Case Study of Recurrent Artificial Neural Networks1997Report (Other academic)
    Abstract [en]

    This paper summarizes and analyzes the results of a case study of artificial neural networks for the detection of oil spills from radar imagery, which has been carried as a joint project between the Connectionist Research Group, University of Skövde, and Ericsson Microwave Systems AB, Mölndal, Sweden.

12 51 - 74 of 74
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf