Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

Ishibuchi, Hisao; Yamamoto, Takashi

doi:10.1007/3-540-45105-6_117

Cited by 26 publications

(7 citation statements)

References 22 publications

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“…Table 5 demonstrates the average accuracy values of the classifiers found on test data. It also compares the performance of our method with the well-known approach, Ishibuchi et al's method [17]. From Table 5, we can see that the performance of our classifiers was much better than the results of the other algorithm.…”

Section: Resultsmentioning

confidence: 65%

Extraction of classification rules from socio-demographics and biochemistry datasets of schizophrenia patients using multi-objective genetic algorithms

Kaya

Türkoğlu

2013

2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS)

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This paper presents a method for extracting automatically classification rules via multi-objective genetic algorithms. The paper also proposes a novel objective measure to quantify the similarity of the rules. The other objectives of the rules are average support value and accuracy. We experimentally evaluate our approach on socio-demographics and biochemistry datasets of schizophrenia patients and demonstrate that our algorithm encourages us to improve and apply this strategy in many real-world applications.

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Section: Resultsmentioning

confidence: 65%

Extraction of classification rules from socio-demographics and biochemistry datasets of schizophrenia patients using multi-objective genetic algorithms

Kaya

Türkoğlu

2013

2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS)

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“…Pareto-based generation of ensembles for radial basis function networks [60] and fuzzy rule systems [61] have also been reported.…”

Section: Diverse Ensemble Generationmentioning

confidence: 99%

Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

Jin

Sendhoff

2008

IEEE Trans. Syst., Man, Cybern. C

336

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Abstract-Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can be mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function. Over the last decade, efforts on solving machine learning problems using the Pareto-based multiobjective optimization methodology have gained increasing impetus, particularly due to the great success of multiobjective optimization using evolutionary algorithms and other population-based stochastic search methods. It has been shown that Pareto-based multiobjective learning approaches are more powerful compared to learning algorithms with a scalar cost function in addressing various topics of machine learning, such as clustering, feature selection, improvement of generalization ability, knowledge extraction, and ensemble generation. One common benefit of the different multiobjective learning approaches is that a deeper insight into the learning problem can be gained by analyzing the Pareto front composed of multiple Pareto-optimal solutions. This paper provides an overview of the existing research on multiobjective machine learning, focusing on supervised learning. In addition, a number of case studies are provided to illustrate the major benefits of the Pareto-based approach to machine learning, e.g., how to identify interpretable models and models that can generalize on unseen data from the obtained Pareto-optimal solutions. Three approaches to Pareto-based multiobjective ensemble generation are compared and discussed in detail. Finally, potentially interesting topics in multiobjective machine learning are suggested.

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“…This research was continued, for instance, by Perrone [20] and Fillipi et al [21]. Using an ensemble (mixture of predictors) usually results in an improvement in the prediction accuracy [22]; this is specially true if the individual predictors are unstable, that is, different predictors are obtained in each instance of training. Several general and constructive methods have been proposed to create these ensembles: boosting, originally proposed by Schapire [23], which creates each classifier taking into account the errors of the previous one, and bagging, proposed by Breiman et al [24], which uses different partitions of the original training set to train each predictor in the ensemble.…”

Section: Introductionmentioning

confidence: 97%

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

Alfaro-Cid

Valdivieso

Esparcia

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.

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Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

Cited by 26 publications

References 22 publications

Extraction of classification rules from socio-demographics and biochemistry datasets of schizophrenia patients using multi-objective genetic algorithms

Extraction of classification rules from socio-demographics and biochemistry datasets of schizophrenia patients using multi-objective genetic algorithms

Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

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