1,721,195 research outputs found
Corrections to "Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies" [May 08 397-415].
No full textJin Y, Sendhoff B. Corrections to “Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies” [May 08 397-415]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2009;39(3):373-373.In the above titled paper (ibid., vol. 38, no. 3, pp. 397-415, May 08), there are three sites where an inequality is put wrongly. The corrections are presented here
Comparing neural networks and Kriging for fitness approximation in evolutionary optimization
No full textWillmes L, Back T, Jin Y, Sendhoff B. Comparing neural networks and Kriging for fitness approximation in evolutionary optimization. In: The 2003 Congress on Evolutionary Computation, 2003. CEC '03. IEEE; 2003: 663-670.Neural networks and Kriging method are compared for constructing fitness approximation models in evolutionary optimization algorithms. The two models are applied in an identical framework to the optimization of a number of well known test functions. In addition, two different ways of training the approximators are evaluated: in one setting the models are built off-line using data from previous optimization runs and in the other setting the models are built online from the data available from the current optimization
Pareto-based multiobjective machine learning: An overview and case studies
No full textJin Y, Sendhoff B. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2008;38(3):397-415.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
A Hybrid Object Recognition Architecture
No full textHeidemann G, Kummert F, Ritter H, Sagerer G. A Hybrid Object Recognition Architecture. In: von der Malsburg C, von Seelen W, Vorbrüggen JC, Sendhoff B, eds. Artificial Neural Networks — ICANN 96, 16.-19. July. Springer-Verlag, Berlin; 1996: 305-310
An examination of different fitness and novelty based selection methods for the evolution of neural networks
No full textInden B, Jin Y, Haschke R, Ritter H, Sendhoff B. An examination of different fitness and novelty based selection methods for the evolution of neural networks. Soft Computing. 2013;17(5):753-767
An approach to rule-based knowledge extraction
No full textJin Y, von Seelen W, Sendhoff B. An approach to rule-based knowledge extraction. In: 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228). Vol 2. IEEE; 1998: 1188-1193.The extraction of easily interpretable knowledge from the large amount of data measured in experiments is very desirable. This paper proposes a method to achieve this. A fuzzy rule system is first generated and optimized using evolution strategies. This fuzzy system is then converted to an RBF neural network to refine the obtained knowledge. In order to extract understandable fuzzy rules from the trained RBF network, a neural network regularization technique called adaptive weight sharing is developed. Simulation results on the Mackey-Glass system show that the proposed approach to knowledge extraction is effective and practical
Evolutionary multi-objective optimisation with a hybrid representation
No full textOkabe T, Jin Y, Sendhoff B. Evolutionary multi-objective optimisation with a hybrid representation. In: The 2003 Congress on Evolutionary Computation, 2003. CEC '03. Vol 4. IEEE; 2003: 2262-2269.For tackling multiobjective optimisation (MOO) problem, many methods are available in the field of evolutionary computation (EC). To use the proposed method(s), the choice of the representation should be considered first. In EC, often binary representation and real-valued representation are used. We propose a hybrid representation, composed of binary and real-valued representations for multi-objective optimisation problems. Several issues such as discretisation error in the binary representation, self-adaptation of strategy parameters and adaptive switching of representations are addressed. Experiments are conducted on five test functions using six different performance indices, which shows that the hybrid representation exhibits better and more stable performance than the single binary or real-valued representation
Emergence of feedback in artificial gene regulatory networks
No full textSteiner T, Schramm L, Jin Y, Sendhoff B. Emergence of feedback in artificial gene regulatory networks. In: 2007 IEEE Congress on Evolutionary Computation. IEEE; 2007: 867-874.In this paper, we present a model for simulating the evolution of development together with a method for the analysis of emergence of negative feedback inside the regulatory network. In order to record the development of feedback during evolution, we analyze both the static as well as the dynamic interactions between the transcription factors in the regulatory network. When perturbing the gene regulatory network using random mutations, we find that the evolved negative feedback is the main mechanism for robustness against such mutations. We argue that this robustness is the reason for the sustained emergence of negative feedback during evolution
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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