Andreas Stafylopatis

Fuzzy neural network-based texture analysis of ultrasonic images

The efficacy of a novel fuzzy neural network classifier for the characterization of ultrasonic liver images based on texture analysis techniques is investigated. Classification features are extracted with the use of image texture analysis techniques such as fractal dimension texture analysis, spatial gray-level dependence matrices, gray-level difference statistics, gray-level run-length statistics, and first-order gray-level parameters. These features are fed to a neural network classifier based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed, leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed.

A clustering method based on boosting

It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g.k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality.

Global behavior of homogeneous random neural systems

Abstract We define a simple form of homogeneous neural network model whose characteristics are expressed in terms of probabilistic assumptions. The networks considered operate in an asynchronous manner and receive the influence of the environment in the form of external stimulations. The operation of the network is described by means of a Markovian process whose steady-statesolution yields several global measures of the networks activity. Three different types of external stimulations are investigated, which represent possible input mechanisms. The analytical results obtained concern the macroscopic viewpoint and provide a quick insight into the structure of the networks behavior.

A hybrid movie recommender system based on neural networks

Recently, there has been a lot of speculation among the members of the artificial intelligence community concerning the way AI can help with the problem of successful information search in the reservoirs of knowledge of Internet. Recommender systems provide a solution to this problem by giving individualized recommendations. Content-based and collaborative filtering are usually applied to predict these recommendations. A combination of the results of these two techniques is used in this work in order to construct a system that provides more precise recommendations concerning movies. The MovieLens data set was used to test the proposed hybrid system.

Training the random neural network using quasi-Newton methods

Abstract Training in the random neural network (RNN) is generally specified as the minimization of an appropriate error function with respect to the parameters of the network (weights corresponding to positive and negative connections). We propose here a technique for error minimization that is based on the use of quasi-Newton optimization techniques. Such techniques offer more sophisticated exploitation of the gradient information compared to simple gradient descent methods, but are computationally more expensive and difficult to implement. In this work we specify the necessary details for the application of quasi-Newton methods to the training of the RNN, and provide comparative experimental results from the use of these methods to some well-known test problems, which confirm the superiority of the approach.

A divide-and-conquer method for multi-net classifiers

Abstract Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the ‘divide-and-conquer’ framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subtasks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).

A Multi-clustering Fusion Algorithm

A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Finally, the fusion procedure starts with the clusters produced by the combining part and finds the optimal number of clusters in the data set according to some predefined criteria. The unsupervised multi-clustering method implemented in this work is quite general. There is ample room for the implementation and testing with any existing clustering algorithm that has unstable results. Experiments using both simulated and real data sets indicate that the multi-clustering fusion algorithm is able to partition a set of data points to the optimal number of clusters not constrained to be hyperspherically shaped.

Autonomous vehicle navigation using evolutionary reinforcement learning

Reinforcement learning schemes perform direct on-line search in control space. This makes them appropriate for modifying control rules to obtain improvements in the performance of a system. The effectiveness of a reinforcement learning strategy is studied here through the training of a learning classifier system (LCS) that controls the movement of an autonomous vehicle in simulated paths including left and right turns. The LCS comprises a set of condition-action rules (classifiers) that compete to control the system and evolve by means of a genetic algorithm (GA). Evolution and operation of classifiers depend upon an appropriate credit assignment mechanism based on reinforcement learning. Different design options and the role of various parameters have been investigated experimentally. The performance of vehicle movement under the proposed evolutionary approach is superior compared with that of other (neural) approaches based on reinforcement learning that have been applied previously to the same benchmark problem.

Data Mining based on Gene Expression Programming and Clonal Selection

A hybrid evolutionary technique is proposed for data mining tasks, which combines the Clonal Selection Principle with Gene Expression Programming (GEP). The proposed algorithm introduces the notion of Data Class Antigens, which is used to represent a class of data. The produced rules are evolved by a clonal selection algorithm, which extends the recently proposed CLONALG algorithm. In the present algorithm, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies, which are coded as GEP chromosomes, in order to exploit the flexibility and the expressiveness of such encoding. The algorithm is tested on some benchmark problems of the UCI repository, and in particular on the set of MONK problems and the Pima Indians Diabetes problem. In both problems, the results in terms of prediction accuracy are very satisfactory, albeit slightly less accurate than those obtained by a standard GEP technique. In terms of convergence rate and computational efficiency, however, the technique proposed here markedly outperforms the standard GEP algorithm.

A Review of Decision Support Systems in Telecare

This paper presents an overview of the state-of-the-art on decision support systems (DSS) in telecare. The main aspect examined is the use of smaller subsystems—components in an integrated DSS, with emphasis on two application areas: medical home unit monitoring and real-time prioritisation of the alerts generated by them, and drug interaction checking. The paper suggests the development of an integrated hybrid telecare DSS synthesizing most of the technologies reviewed. Implementation issues are also examined, with an emphasis on the international trend towards the development of platform-independent, component-based, distributed software.