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The selection of a particular neural network model belonging to the Pareto front is a problem that exists in all multi-objective algorithms. This paper proposes a novel solution to this problem based on a linear combination of the outputs of the two extremes in the Pareto front, which form an ensemble. The decision support TOPSIS method is used to determine which linear combination creates the best...
Use of unmanned Aerial Vehicles (UAVs) has gained significant importance in the recent years because of their ability to remotely monitor and perform various tasks in an autonomous manner. However, the control unit of such UAVs fails to adapt quickly when the UAVs are exposed to unpredictable and violent external disturbances such as violent wind gusts and extreme weather conditions. The cost of such...
In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles...
Paper deals with the problem of designing efficient classifiers for a special case of incremental concept drift. We focus on its classification based on the multiple classifier system. For the problem under consideration we propose four simple methods of combining classification and evaluate them via computer experiments.
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is...
This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional...
A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has...
Signature is a popular method of seeking approval and authentication between various parties in many transaction applications. Signature pattern recognition is done by processing a set of data that consists of (x, y) coordinates, representing online signature. Particle Swarm Optimisation technique is used to find and analyse the baseline feature that exists within the signature. Signatures were taken...
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