Evolutionary cellular configurations for designing feed-forward neural networks architectures

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward neural networks has increased. Most of the methods are based on evolutionary computation paradigms. Some of the designed methods are based on direct representations of the parameters of the network. These representations do not allow scalability, so to represent large architectures, very large structures are required. An alternative more interesting are the indirect schemes. They codify a compact representation of the neural network. In this work, an indirect constructive encoding scheme is presented. This scheme is based on cellular automata representations in order to increase the scalability of the method

Evolutionary approach to overcome initialization parameters in classification problems

Proceeding of: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003 Maó, Menorca, Spain, June 3–6, 2003.The design of nearest neighbour classifiers is very dependent from some crucial parameters involved in learning, like the number of prototypes to use, the initial localization of these prototypes, and a smoothing parameter. These parameters have to be found by a trial and error process or by some automatic methods. In this work, an evolutionary approach based on Nearest Neighbour Classifier (ENNC), is described. Main property of this algorithm is that it does not require any of the above mentioned parameters. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and emerging a high classification accuracy for the whole classifier

Automatic symbolic modelling of co-evolutionarily learned robot skills

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001Evolutionary based learning systems have proven to be very powerful techniques for solving a wide range of tasks, from prediction to optimization. However, in some cases the learned concepts are unreadable for humans. This prevents a deep semantic analysis of what has been really learned by those systems. We present in this paper an alternative to obtain symbolic models from subsymbolic learning. In the first stage, a subsymbolic learning system is applied to a given task. Then, a symbolic classifier is used for automatically generating the symbolic counterpart of the subsymbolic model. We have tested this approach to obtain a symbolic model of a neural network. The neural network defines a simple controller af an autonomous robot. a competitive coevolutive method has been applied in order to learn the right weights of the neural network. The results show that the obtained symbolic model is very accurate in the task of modelling the subsymbolic system, adding to this its readability characteristic

Heterogeneous Kohonen networks

A large number of practical problems involves elements that are described as a mixture of qualitative and quantitative infomation, and whose description is probably incomplete. The self-organizing map is an effective tool for visualization of high-dimensional continuous data. In this work, we extend the network and training algorithm to cope with heterogeneous information, as well as missing values. The classification performance on a collection of benchmarking data sets is compared in different configurations. Various visualization methods are suggested to aid users interpret post-training results.Peer ReviewedPostprint (author's final draft

Estimation of instrinsic dimension via clustering

The problem of estimating the intrinsic dimension of a set of points in high dimensional space is a critical issue for a wide range of disciplines, including genomics, finance, and networking. Current estimation techniques are dependent on either the ambient or intrinsic dimension in terms of computational complexity, which may cause these methods to become intractable for large data sets. In this paper, we present a clustering-based methodology that exploits the inherent self-similarity of data to efficiently estimate the intrinsic dimension of a set of points. When the data satisfies a specified general clustering condition, we prove that the estimated dimension approaches the true Hausdorff dimension. Experiments show that the clustering-based approach allows for more efficient and accurate intrinsic dimension estimation compared with all prior techniques, even when the data does not conform to obvious self-similarity structure. Finally, we present empirical results which show the clustering-based estimation allows for a natural partitioning of the data points that lie on separate manifolds of varying intrinsic dimension

Ontology of zakat management system

Zakat Management System is a system that manages all the processes that are involved in zakat activities.At present, there exist no standard which can be utilized to develop Zakat Management System. In order to support the development of Zakat Management System, this paper provides the ontology of Zakat Management System aimed specifically to share the knowledge of zakat. Each person who are involved in the development of this system will hopefully share a common understanding of Zakat Management System. This in turn will make the process of development faster

A Meta-Model Ontology based on Scenarios

This paper describes a proof-of-concept designed to test a meta-model ontology based on scenarios for representing the procedural knowledge of a given domain. The research was conducted as part of a study on methodologies to build ontologies for Information Systems. The frequent use of scenarios in the initial steps of the methodologies motivated us to investigate its use in the process of building ontologies for IS modeling. This research proposes the use of the components of scenarios as constructs of a meta-model ontology. With this approach, the process of building scenarios about a domain is mingled with the process of building domain ontologies

Fuzzy Analysis of Breast Cancer Disease using Fuzzy c-means and Pattern Recognition

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. The automatic diagnosis of breast cancer is an important, real-world medical problem. In this article is introduced a new approach for diagnosis of breast cancer. The proposed approach uses Fuzzy c-means (FCM) algorithm and pattern recognition method. Algorithm has been applied to breast cancer clinic instances obtained from the University of Wisconsin. Using FCM algorithm clinic instances are grouped into two clusters, one with benign instances and other with malign instances. Further, input data are divided in train data and test data and success of each is evaluated. In pattern recognition method each input test data is assigned to one of the clusters obtained from the process of FCM classification. The proposed system has showed that the recommended system has a high accuracy