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Sökning: WAKA:kon > Högskolan i Borås

  • Resultat 1781-1790 av 3351
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1781.
  • König, Rikard, et al. (författare)
  • Improving GP Classification Performance by Injection of Decision Trees
  • 2010
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a novel hybrid method combining genetic programming and decision tree learning. The method starts by estimating a benchmark level of reasonable accuracy, based on decision tree performance on bootstrap samples of the training set. Next, a normal GP evolution is started with the aim of producing an accurate GP. At even intervals, the best GP in the population is evaluated against the accuracy benchmark. If the GP has higher accuracy than the benchmark, the evolution continues normally until the maximum number of generations is reached. If the accuracy is lower than the benchmark, two things happen. First, the fitness function is modified to allow larger GPs, able to represent more complex models. Secondly, a decision tree with increased size and trained on a bootstrap of the training data is injected into the population. The experiments show that the hybrid solution of injecting decision trees into a GP population gives synergetic effects producing results that are better than using either technique separately. The results, from 18 UCI data sets, show that the proposed method clearly outperforms normal GP, and is significantly better than the standard decision tree algorithm.
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1782.
  • König, Rikard, et al. (författare)
  • Instance Ranking Using Ensemble Spread
  • 2007
  • Ingår i: Proceedings of the 2007 International Conference on Data Mining. - : CSREA Press. - 1601320310 - 9781601320315 ; , s. 73-78
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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1783.
  • König, Rikard, et al. (författare)
  • Modeling golf player skill using machine learning
  • 2017
  • Ingår i: Machine Learning and Knowledge Extraction. - Cham : Springer. - 9783319668079 ; , s. 275-294
  • Konferensbidrag (refereegranskat)abstract
    • In this study we apply machine learning techniques to Modeling Golf Player Skill using a dataset consisting of 277 golfers. The dataset includes 28 quantitative metrics, related to the club head at impact and ball flight, captured using a Doppler-radar. For modeling, cost-sensitive decision trees and random forest are used to discern between less skilled players and very good ones, i.e., Hackers and Pros. The results show that both random forest and decision trees achieve high predictive accuracy, with regards to true positive rate, accuracy and area under the ROC-curve. A detailed interpretation of the decision trees shows that they concur with modern swing theory, e.g., consistency is very important, while face angle, club path and dynamic loft are the most important evaluated swing factors, when discerning between Hackers and Pros. Most of the Hackers could be identified by a rather large deviation in one of these values compared to the Pros. Hackers, which had less variation in these aspects of the swing, could instead be identified by a steeper swing plane and a lower club speed. The importance of the swing plane is an interesting finding, since it was not expected and is not easy to explain. © 2017, IFIP International Federation for Information Processing.
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1784.
  • König, Rikard, et al. (författare)
  • Rule Extraction using Genetic Programming for Accurate Sales Forecasting
  • 2014
  • Konferensbidrag (refereegranskat)abstract
    • The purpose of this paper is to propose and evaluate a method for reducing the inherent tendency of genetic programming to overfit small and noisy data sets. In addition, the use of different optimization criteria for symbolic regression is demonstrated. The key idea is to reduce the risk of overfitting noise in the training data by introducing an intermediate predictive model in the process. More specifically, instead of directly evolving a genetic regression model based on labeled training data, the first step is to generate a highly accurate ensemble model. Since ensembles are very robust, the resulting predictions will contain less noise than the original data set. In the second step, an interpretable model is evolved, using the ensemble predictions, instead of the true labels, as the target variable. Experiments on 175 sales forecasting data sets, from one of Sweden’s largest wholesale companies, show that the proposed technique obtained significantly better predictive performance, compared to both straightforward use of genetic programming and the standard M5P technique. Naturally, the level of improvement depends critically on the performance of the intermediate ensemble.
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1785.
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1786.
  • König, Rikard, et al. (författare)
  • Using Genetic Programming to Increase Rule Quality
  • 2008
  • Ingår i: Proceedings of the Twenty-First International FLAIRS Conference (FLAIRS 2008). - : AAAI Press. - 9781577353652 ; , s. 288-293
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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1787.
  • Lagnevik, Magnus, et al. (författare)
  • Innovation community governance The case of Skåne Food Innovation Network
  • 2010
  • Konferensbidrag (refereegranskat)abstract
    • Purpose – The aim of the paper is to investigate the emerging and subtle role of the intermediaries or knowledge brokers and how to increase the learning and development of the innovation intermediaries with a governance perspective. Design/methodology/approach – The paper is the elaboration of the Mintzberg idea of rebuilding companies as communities to foster innovation in communities by using specially designed meeting format called systemic meeting. Findings – There are emerging patterns of the firms creating value networks to accelerate innovation and share knowledge. Intermediaries or knowledge brokers acting as an interface between seekers and solvers will need different mindset or approach to innovation with more of governance perspective than that of government. Originality/value – This paper extends the understanding of the intermediaries and proposed a new systemic approach to the Innovation intermediaries.
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1788.
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1789.
  • Landahl, Karin (författare)
  • Form is making
  • 2012
  • Ingår i: Form is making.
  • Konferensbidrag (refereegranskat)
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1790.
  • Landahl, Karin (författare)
  • Knitted Knots
  • 2015
  • Ingår i: Knitted Knots.
  • Konferensbidrag (refereegranskat)
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