Catálogo de publicaciones - libros

A successful adoption and adaptation of the particle swarm optimization (PSO) algorithm is presented in this paper. It improves the performance of Support Vector Machine (SVM) in the classification of incipient faults of power transformers. A PSO-based encoding technique is developed to improve the accuracy of classification. The proposed scheme is capable of removing misleading input features and, optimizing the kernel parameters at the same time. Experiments on real operational data had demonstrated the effectiveness and efficiency of the proposed approach. The power system industry can benefit from our system in both the accelerated operational speed and the improved accuracy in the classification of incipient faults.

Large amounts of spatially referenced data have been aggregated in various application domains such as Geographic Information Systems (GIS), banking and retailing that motivate the highly demanding field of spatial data mining. So far many beneficial optimization solutions have been introduced inspired by the foraging behavior of ant colonies. In this paper a novel algorithm named AntTrend is proposed for efficient discovery of . AntTrend applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this valuable knowledge. Ant agents in AntTrend share their individual experience of trend detection by exploiting the phenomenon of . Many experiments were run on a real banking spatial database to investigate the properties of the algorithm. The results show that AntTrend has much higher efficiency both in performance of the discovery process and in the quality of patterns discovered compared to non-intelligent methods.

Multiple UAV cooperative reconnaissance is one of the most important aspects of UAV operations. This paper presents a genetic algorithm(GA) based approach for multiple UAVs cooperative reconnaissance mission planning problem. The objective is to conduct reconnaissance on a set of targets within predefined time windows at minimum cost, while satisfying the reconnaissance resolution demands of the targets, and without violating the maximum travel time for each UAV. A mathematical formulation is presented for the problem, taking the targets reconnaissance resolution demands and time windows constraints into account, which are always ignored in previous approaches. Then a GA based approach is put forward to resolve the problem. Our GA implementation uses integer string as the chromosome representation, and incorporates novel evolutionary operators, including a subsequence crossover operator and a forward insertion mutation operator. Finally the simulation results show the efficiency of our algorithm.

This paper deals with the problem of training a discriminative classifier when the data sets are imbalanced. More specifically, this work is concerned with the problem of classify a sample as belonging, or not, to a Target Class (TC), when the number of examples from the “Non-Target Class” (NTC) is much higher than those of the TC. The effectiveness of the heuristic method called (NTIL) in the task of extracting, from the pool of NTC representatives, the most discriminant training subset with regard to the TC, has been proved when an Artificial Neural Network is used as classifier (ISMIS 2003). In this paper the effectiveness of this method is also shown for Support Vector Machines.

In the paper, a new method for cost-sensitive learning of decision trees is proposed. Our approach consists in extending the existing evolutionary algorithm (EA) for global induction of decision trees. In contrast to the classical top-down methods, our system searches for the whole tree at the moment. We propose a new fitness function which allows the algorithm to minimize expected cost of classification defined as a sum of misclassification cost and cost of the tests. The remaining components of EA i.e. the representation of solutions and the specialized genetic search operators are not changed. The proposed method is experimentally validated and preliminary results show that the global approach is able to effectively induce cost-sensitive decision trees.

The search for an optimal node elimination sequence for the triangulation of Bayesian networks is an NP-hard problem. In this paper, a new method, called the TAGA algorithm, is proposed to search for the optimal node elimination sequence. TAGA adjusts the probabilities of crossover and mutation operators by itself, and provides an adaptive ranking-based selection operator that adjusts the pressure of selection according to the evolution of the population. Therefore the algorithm not only maintains the diversity of the population and avoids premature convergence, but also improves on-line and off-line performances. Experimental results show that the TAGA algorithm outperforms a simple genetic algorithm, an existing adaptive genetic algorithm, and simulated annealing on three Bayesian networks.

Electronic markets with access to the Internet and the World Wide Web, are information-rich and require agents that can assimilate and use real-time information flows wisely. A new breed of “information-based” agents aims to meet this requirement. They are founded on concepts from information theory, and are designed to operate with information flows of varying and questionable integrity. These agents are part of a larger project that aims to make informed automated trading in applications such as eProcurement a reality.

This paper aims at studying the possibility of exploiting the Intelligent Agent technology in e-government for supporting the decision making activity of government agencies. Specifically, it proposes a system to assist managers of a government agency, who plan to propose a new service, to identify those citizens that could gain the highest benefit from it. The paper illustrates the proposed system and reports some experimental results.

This paper illustrates our work concerning the development of an agent-based architecture for the control of a smart home environment. In particular, we focus the description on a particular component of the system: the Butler Interactor Agent (BIA). BIA has the role of mediating between the agents controlling environment devices and the user. As any good butler, it is able to observe and learn about users preferences but it leaves to its “owner” the last word on critical decisions. This is possible by employing user and context modeling techniques in order to provide a dynamic adaptation of the interaction with the environment according to the vision of ambient intelligence. Moreover, in order to support trust, this agent is able to adapt its autonomy on the basis of the received user delegation.

Continuously monitoring large-scale aggregates over data streams is important for many stream processing applications, e.g. collaborative intelligence analysis, and presents new challenges to data management systems. The first challenge is to efficiently generate the updated aggregate values and provide the new results to users after new tuples arrive. We implemented an incremental aggregation mechanism for doing so for arbitrary algebraic aggregate functions including user-defined ones by keeping up-to-date finite data summaries. The second challenge is to construct shared query evaluation plans to support large-scale queries effectively. Since multiple query optimization is NP-complete and the queries generally arrive asynchronously, we apply an incremental sharing approach to obtain the shared plans that perform reasonably well. The system is built as a part of ARGUS, a stream processing system atop of a DBMS. The evaluation study shows that our approaches are effective and efficient on typical collaborative intelligence analysis data and queries.