Catálogo de publicaciones - libros

In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a ${\mathcal MAX}-{\mathcal MIN}$ ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules resulting in less and shorter rules. Our experiments show a significant improvement of the performance both in accuracy and comprehensibility, compared to previous data mining techniques based on ant systems and other state-of-the-art classification techniques.

There have been many technical trading rules in stock market since the first stock exchange founded. Along with the developing of computer technology, the technical trading rules are playing more and more important roles in the stock market trading system. However, there are many problems also occurred, such as the huge database, inefficiency, etc. So, the in-depth data mining technology is becoming a powerful tool to overcome the shortage of the current technologies. In this paper, we give some applications of in-depth data mining method: to find the optimal range, to find the stock-rule pair and find the relationship between the number of pair and investment. This method can improve both efficiency and effectiveness.

Adopting one of human brain’sfunction ,we designed Intelligent Knowledge Capsule with Selective Masking Matrix for efficient data selection which has the hierarchical structure, learning, perception and knowledge retrieval mechanism. This system was applied to the virtual memory and tested.

In offline opponent modeling, large datasets can be utilized as training data to model the opponent. In the Coach competition of RoboCup Soccer, offline opponent modeling can be adopted to train the coach learn about the opponent’s behavior patterns. Data-mining techniques, particularly decision-tree construction can be applied in identifying interesting behavior patterns of the opponent. This research explores the use of the decision-tree algorithm C4.5 to generate classification rules that will embody the offensive and defensive strategies (plans) of the coach against its opponent(s). To achieve this objective, the SimSoccer Coach system is built.

Both automated design and knowledge discovery of electronic circuits are challenging tasks for artificial intelligence. A genetic algorithm (GA) based approach to them was proposed in this paper, which features an array-based encoding scheme, a multi-objective evaluation mechanism and an adaptation strategy for GA parameters. It was validated by the experiments on arithmetic circuits of gradually increasing scales, which evolved multi-objective optimized circuits and revealed some novel and generalized principles.

It is known that algorithms for discovering association rules generate an overwhelming number of those rules. While many new very efficient algorithms were recently proposed to allow the mining of extremely large datasets, the problem due to the sheer number of rules discovered still remains. In this paper we propose a new way of pushing the constraints in dual-mode based from the set of maximal patterns that is an order of magnitude smaller than the set of all frequent patterns.

It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in information gathering) since duplications and ambiguities of data values (terms). This paper presents a novel concept of rough association rules to improve the quality of discovered knowledge. The precondition of a rough association rule consists of a set of terms (items) and a weight distribution of terms (items). The distinct advantage of rough association rules is that they contain more specific information than normal association rules.

An important issue in multi-channel manufacturing (MCM) design is the channel formatio n process. In this study, the control parameters that affect the performance of genetic algorithms (GAs) developed to solve channel formation problem, are examined and the optimum values of such parameters are explored using Taguchi method. Two types of problems were taken into account in terms of machines, parts, and channels. Experimental results show that the performance of a GA significantly dependent on the levels of the design factors for the problem being solved. The results also show that Taguchi method is a powerful approach for identifying design factors suitable for the GA comparing to time consuming and possibly impractical trial-error tests.

In utilities using a mixture of hydroelectric and non-hydroelectric power, the economics of the hydroelectric plants depend upon the reservoir height and the inflow into the reservoir for several months into the future. Accurate forecasts of reservoir inflow allow the utility to feed proper amounts of fuel to individual plants, and to economically allocate the load between various non-hydroelectric plants. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models. This paper provides for river flow prediction a numerical comparison between constructive neural networks and PARMA models. The results obtained in the evaluation of the performance of Neural Network were better than the results obtained with PARMA models.

In this paper, a novel license plate location algorithm for color image is presented. Firstly the neural networks are used as filters for analyzing within small windows for an image and deciding whether each window contains a license plate or not coarsely. And then we use the information which the license plate’s saturation value is different from the background’s, so it can be used to locate license plate finely. At last, color pairs method is presented to prove whether the region we found is the license plate region or not. The experimental results show that proposed algorithms are robust in dealing with the license plate location in complex background.