Improving multiclass pattern recognition by the combination of two strategies

We present a new method of multiclass classification based on the combination of one- vs- all method and a modification of one- vs- one method. This combination of one- vs- all and one- vs- one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided

Cooperative coevolution of artificial neural network ensembles for pattern classification

This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many real-world problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten real-world classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller

CRIBEL: Lifelong learning social network governed by academic institutions: an affordable serverless model in the cloud

Academic institutions, teachers, students and workers are facing important changes in the way they organise work and training in new skills demanded by companies in the face of the new challenges of society in general due to the digital transition. One of the accepted strategies for these subjects is the Lifelong Learning model that prepares them in knowledge, skills and competences they need to thrive in the labour market and in their personal and private life. Communication and motivation are key elements in the educational process in general and in Lifelong Learning in particular, and it is here where online social networks services (SNSs) are a tool of great potential in this sense given their capacity to offer information and contents, as well as to facilitate the interconnection between subjects involved in the process. But, are popular and public SNSs sites like Facebook, Twitter or Youtube the networks we need in our academic institutions? SNSs including Facebook, Youtube, and Twitter are widely used for educational purposes. And they have potential and interesting aspects. But of course, there are also difficulties associated with the use of these online social networks by academic institutions. Among the difficulties mentioned, we will focus on three categories. The first is the loss of student time filtering high quality and useful content for their training and the lack of expert guidance in this task. The second is the lack of control of the social network by the academic institution, which has little power of governance and decision-making in a social network such as Facebook or Twitter, governed by private entities according to non-educational interests. And the third is the economic cost and technical challenge for an academic institution to create and maintain its own online social network. We present a model of a new social network oriented to Lifelong Learning we have named Cribel, whose main characteristics are its simplicity and agility, its motivational elements of the user experience, the control of the quality of the contents, and its simplicity and low cost of implementation, maintenance, and automatic scaling due to its serverless design model in the cloud

CIXL2 - A crossover operator for evolutionary algorithms based on population features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods. 1