Catálogo de publicaciones - libros

In this chapter, we present a multiobjective evolutionary algorithm based design procedure for radial-basis function neural networks. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is proposed to evolve the neural network’s topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to tradeoff between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three state-of-the-art designs for radial-basis function neural networks to predict Mackey-Glass chaotic time series.

Tree induction algorithms use heuristic information to obtain decision tree classification. However, there has been little research on how many rules are appropriate for a given set of data, that is, how we can find the best structure leading to desirable generalization performance. In this chapter, an evolutionary multi-objective optimization approach with genetic programming will be applied to the data classification problem in order to find the minimum error rate or the best pattern classifier for each size of decision trees. As a result, we can evaluate the classification performance under various structural complexity of decision trees. Following structural risk minimization suggested by Vapnik, we can determine a desirable number of rules with the best generalization performance. The suggested method is compared with C4.5 application for machine learning data.

Learning concept descriptions from data is a complex multiobjective task. The model induced by the learner should be accurate so that it can represent precisely the data instances, complete , which means it can be generalizable to new instances, and minimum , or easily readable. Learning Classifier Systems (LCSs) are a family of learners whose primary search mechanism is a genetic algorithm. Along the intense history of the field, the efforts of the community have been centered on the design of LCSs that solved these goals efficiently, resulting in the proposal of multiple systems. This paper revises the main LCS approaches and focuses on the analysis of the different mechanisms designed to fulfill the learning goals. Some of these mechanisms include implicit multiobjective learning mechanisms, while others use explicit multiobjective evolutionary algorithms. The paper analyses the advantages of using multiobjective evolutionary algorithms, especially in Pittsburgh LCSs, such as controlling the so-called bloat effect, and offering the human expert a set of concept description alternatives.

Generating machine learning models is inherently a multi-objective optimization problem. Two most common objectives are accuracy and interpretability, which are very likely conflicting with each other. While in most cases we are interested only in the model accuracy, interpretability of the model becomes the major concern if the model is used for data mining or if the model is applied to critical applications. In this chapter, we present a method for simultaneously generating accurate and interpretable neural network models for classification using an evolutionary multi-objective optimization algorithm. Lifetime learning is embedded to fine-tune the weights in the evolution that mutates the structure and weights of the neural networks. The efficiency of Baldwin effect and Lamarckian evolution are compared. It is found that the Lamarckian evolution outperforms the Baldwin effect in evolutionary multi-objective optimization of neural networks. Simulation results on two benchmark problems demonstrate that the evolutionary multi-objective approach is able to generate both accurate and understandable neural network models, which can be used for different purpose.

The chapter presents a new method of rule extraction from trained neural networks, based on a hierarchical multiobjective genetic algorithm. The problems associated with rule extraction, especially its multiobjective nature, are described in detail, and techniques used when approaching them with genetic algorithms are presented. The main part of the chapter contains a thorough description of the proposed method. It is followed by a discussion of the results of experimental study performed on popular benchmark datasets that confirm the method’s effectiveness.

Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature.

Autonomous temporal linguistic rule extraction is an application of growing interest due to its relevance to both decision support systems and fuzzy controllers. In the presented work, rules are evaluated using three qualitative metrics based on their representation on the truth space diagram. Performance metrics are then treated as competing objectives and Multiple Objective Evolutionary Algorithm is used to search for an optimal set of non-dominated rules. Novel techniques for data pre-processing and rule set post-processing are developed that deal directly with the delays involved in dynamic systems. Data collected from a simulated hot and cold water mixer and a two-phase vertical column is used to validate the proposed procedure.

This chapter discusses the interpretability of Takagi-Sugeno (TS) fuzzy systems. A new TS fuzzy model, whose membership functions are characterized by linguistic modifiers, is presented. The tradeoff between global approximation and local model interpretation has been achieved by minimizing a multiple objective performance measure. In the proposed model, the local models match the global model well and the erratic behaviors of local models are remedied effectively. Furthermore, the transparency of partitioning of input space has been improved during parameter adaptation.

The whole is greater than the sum of the parts; this is the essence of using a mixture of classifiers instead of a single classifier. In particular, an ensemble of neural networks (we call neuro-ensemble) has attracted special attention in the machine learning literature. A set of trained neural networks are combined using a post-gate to form a single super-network. The three main challenges facing researchers in neuro-ensemble are:(1) which network to include in, or exclude from the ensemble; (2) how to define the size of the ensemble; (3) how to define diversity within the ensemble.

Ensembles of learning machines have been formally and empirically shown to outperform (generalise better than) single learners in many cases. Evidence suggests that ensembles generalise better when they constitute members which form a diverse and accurate set. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to generalise better. There exists a trade-off between diversity and accuracy. Multi-objective evolutionary algorithms can be employed to tackle this issue to good effect. This chapter includes a brief overview of ensemble learning in general and presents a critique on the utility of multi-objective evolutionary algorithms for their design. Theoretical aspects of a committee of learners viz. the bias-variance-covariance decomposition and ambiguity decomposition are further discussed in order to support the importance of having both diversity and accuracy in ensembles. Some recent work and experimental results, considering classification tasks in particular, based on multi-objective learning of ensembles are then presented as we examine ensemble formation using neural networks and kernel machines.