Catálogo de publicaciones - libros

The feature selection and weighting are two important parts of automatic text classification. In this paper we give a new method based on concept attributes. We use the Terms of the Chinese word to extract concept attributes, and a Concept Tree (C-Tree) to give these attributes proper weighs considering their positions in the C-Tree, as this information describe the expression powers of the attributes. If these attributes are too weak to sustain the main meanings of the words, they will be deserted and the original word will be reserved. Otherwise, the attributes are selected in stead of the original words. Our main research purpose is to make a balance between concept features and word ones by set a shielded level as the threshold of the feature selection after weighting these features. According to the experiment results, we conclude that we can get enough information from the combined feature set for classification and efficiently reduce the useless features and the noises. In our experiment, the feature dimension is reduced to a much smaller space and the category precise is much better than the word selection methods. By choose different shielded levels, we finally select a best one when the average category precise is up to 93.7%. From the results, we find an extra finding that the precise differences between categories are smaller when we use combined features.

As argued in this paper, a decision support system based on data mining and knowledge discovery is an important factor in improving productivity and yield. The proposed decision support system consists of a neural network model and an inference system based on fuzzy logic. First, the product results are predicted by the neural network model constructed by the quality index of the products that represent the quality of the etching process. And the quality indexes are classified according to and expert’s knowledge. Finally, the product conditions are estimated by the fuzzy inference system using the rules extracted from the classified patterns. We employed data mining and intelligent techniques to find the best condition for the etching process. The proposed decision support system is efficient and easy to be implemented for process management based on an expert’s knowledge.

In this paper, we describe glove-based information retrieval method and input device for wearable computers. We suggest an easy and effective alphanumeric input algorithm using gloves and conduct efficiency test. The key to the development of the proposed device is the use of unique operator-to-key mapping method, key-to-symbol mapping method and simple algorithm. We list and discuss traditional algorithm and method using a glove, then describe an improved newly proposed algorithm using gloves. The efficiency test was conducted and the results were compared with other glove based device and algorithm for wearable computers.

In this paper, a dynamical model for data clustering is proposed. This approach employs a network consisting of interacting elements with each representing an attribute vector of input data and receiving attractions from other elements within a certain region. Those attractions, determined by a predefined similarity measure, drive the elements to converge to their corresponding cluster center. With this model, neither the number of data clusters nor the initial guessing of cluster centers is required. Computer simulations for clustering of real images and Iris data set are performed. The results obtained so far are very promising.

Cellular automata(CA) is not only a discrete dynamical system with infinite dimensions, but also an important computational model. How simple can a CA be and yet support interesting and complicated behavior. There are many unsolved problems in the theory of CA, which appeal many researchers to focus their attentions on the field, especially subclass of CA – linear CA. These studies cover the topological properties, chaotical properties, invertibility, attractors and the classification of linear CA etc.. This is a survey of known results and open questions of D-dimensional linear CA over ℤ.

This paper considers a design framework of a computational experiment in finance. The examination of relationships between statistics used for economic forecasts evaluation and profitability of investment decisions reveals that only the ‘degree of improvement over efficient prediction’ shows robust links with profitability. If profits are not observable, this measure is proposed as an evaluation criterion for an economic prediction. Combined with directional accuracy, it could be used in an estimation technique for economic behavior, as an alternative to conventional least squares. Model discovery and performance surface optimization with genetic algorithm demonstrate profitability improvement with an inconclusive effect on statistical criteria.

One of the most significant factors influencing the liquidity of financial markets is the amount of currency in circulation. Even the central bank is responsible for the distribution of the currency it could not assess the demand for the currency as it is influenced by the non-banking sector. Therefore the amount of currency in circulation have to be forecasted. This paper introduces feedforward structured neural network model and discusses its applicability to the forecasting of the currency in circulation. The forecasting performance of the new neural network model is compared with an ARIMA model. The results indicates that the performance of the neural network model is slightly better and that both models might be applied at least as supportive tools for the liquidity forecasting.

In this paper we firstly analysis the chaotic characters of three sets of the financial time series (Hang Sheng Index (HIS), Shanghai Stock Index and US gold price) based on the phase space reconstruction. But when we adopt the feedforward neural networks to predict those time series, we found this method run short of a criterion in selecting the training set, so we present a new method: using correlation dimension (CD) as the criterion. By the experiments, the method is proved effective.

Artificial Neural Network (ANN) is widely used in the business to get on forecasting, but is often low performance for noisy data. Many techniques have been developed to improve ANN outcomes such as adding more algorithms, feature selection and feature weighting in input variables and modification of input case using instance selection. This paper proposes a Euclidean distance matrix approach to instance selection in ANN for financial forecasting. This approach optimizes a selection task for relevant instance. In addition, the technique improves prediction performance. In this research, ANN is applied to solve problems in forecasting a demand for corporate insurance. This research has compared the performance of forecasting a demand for corporate insurance through two types of ANN models; ANN and ISANN (ANN using Instance Selection supported by Euclidean distance metrics). Using ISANN to forecast a demand for corporate insurance is the most outstanding.

This paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness for data clustering in the initial experiments reported here for three benchmark problems. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. A brief sensitivity analysis of the algorithm was performed in order to investigate the influence of the main user-defined parameters on the learning speed and accuracy of the results presented. General discussions and avenues for future works are also provided.