Catálogo de publicaciones - libros

The paper presents a framework for integrating experts’ opinions from the view of systematic optimization, considering factors of consistency or harmony and reliability of all experts’ opinions. The characteristics of consistence of experts’ opinions are defined based on statistics. The reliability function of expert’s opinion is designed and the measurement of the reliability of integration result of all experts’ opinions was then conducted. A non-liner programming model is set up to get the optimal scheme to correspond to the different experts’ opinions that consider systematically influences of the consistency, harmony and reliabilities at the same time. The independence of each expert’s opinion were not only kept but also, well preserved.

Knowledge Management (KM) with information technology (IT) has made tremendous progresses in recent years. It has helped many people in making decision and transactions. Nevertheless, without continuous expanding and upgrading our habitual domains (HD) and competence set (CS), KM may lead us to decision traps and making wrong decisions. This article introduces the concepts of habitual domains and competence set analysis in such a way that we could see where KM can commit decision traps and how to avoid them. Innovation dynamics, as an overall picture of continued enterprise innovation, is also introduced so that we could know the areas and directions in which KM can make maximum contributions and create value. KM empowered by HD can make KM even more powerful.

The field of Knowledge Management (KM) has already completed its initiatory phase, characterized by operational confusion between knowledge and information, stemming from the tenuous notion of “explicit knowledge”. Consequently, the progress of KM has been much slower than would the significance of knowledge management in a modern enterprise indicate. Here we propose and discuss four cornerstones for returning to the roots of knowledge management and so moving forward towards a new phase of KM. We discuss the roots of reliable knowledge thinking and theory in economics, management and philosophy. Then we formulate clear, unambiguous and pragmatic definitions and distinctions of knowledge and information, establish simple and natural measures of the value of knowledge and propose the Knowledge-Information (KnowIn) continuum and its circulatory nature in managing knowledge of the enterprise. Autopoietic cycle A-C-I-S is elaborated to that purpose. We conclude the paper by discussing some implications of the new KM for strategy and strategic management.

Fuzzy classification proposes an approach to solve uncertainty problem in classification tasks. It assigns an instance to more than one class with different degrees instead of a definite class by crisp classification. This paper studies the usage of fuzzy strategy in classification. Two fuzzy algorithms for sequential self-organizing map and learning vector quantization are proposed based on fuzzy projection and learning rules. The derived classifiers are able to provide fuzzy classes when classifying new data. Experiments show the effectiveness of proposed algorithms in terms of classification accuracy. Keywords: fuzzy classification, self-organizing map (SOM), learning vector quantization (LVQ).

In this paper we first survey the linear programming based discriminant models in the literature. We then propose an interior point algorithm to solve the linear programming. The algorithm is polynomial with simple starting point.

A method for solving optimization problem with continuous parameters using improved ant colony algorithm is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then, crossover and mutation can determine the values of the components in the solution. Our experimental results of the problem of nonlinear programming show that our method has much higher convergence speed and stability than that of GA, and the drawback of ant colony algorithm of not being suitable for solving continuous optimization problems is overcome.

The concept of multi-factors detached coefficients is put forth and an evaluation method is given for customer’s credit in this paper. We also get the optimal multi-factors detached coefficients matrix by minimizing the estimate errors. According to this matrix, we get the scores vector of customer’s credit at n   factors, further the credit of the customers is evaluated and selected by commerce banks.

This paper conducts experiments with three skewed data sets, seeking to demonstrate problems when skewed data is used, and identifying counter problems when data is balanced. The basic data mining algorithms of decision tree, regression-based, and neural network models are considered, using both categorical and continuous data. Two of the data sets have binary outcomes, while the third has a set of four possible outcomes. Key findings are that when the data is highly unbalanced, algorithms tend to degenerate by assigning all cases to the most common outcome. When data is balanced, accuracy rates tend to decline. If data is balanced, that reduces the training set size, and can lead to the degeneracy of model failure through omission of cases encountered in the test set. Decision tree algorithms were found to be the most robust with respect to the degree of balancing applied.

The least square approach is undoubtedly one of the well known methods in the fields of statistics and related disciplines such as optimization, artificial intelligence, and data mining. The core of the traditional least square approach is to find the inverse of the product of the design matrix and its transpose. Therefore, it requires storing at least two matrixes - the design matrix and the inverse matrix of the product. In some applications, for example, high frequency financial data in the capital market and transactional data in the credit card market, the design matrix is huge and on line update is desirable. Such cases present a difficulty to the traditional matrix version of the least square approach. The reasons are from the following two aspects: (1) it is still a cumbersome task to manipulate the huge matrix; (2) it is difficult to utilize the latest information and update the estimates on the fly. Therefore, a new method is demanded. In this paper, authors applied the idea of CIO-component-wise iterative optimization and propose an algorithm to solve a least square estimate without manipulating matrix, i.e. it requires no storage for the design matrix and the inverse of the product, and furthermore it can update the estimates on the fly. Also, it is rigorously shown that the solution obtained by the algorithm is truly a least square estimate.

Researchers often have several different hypothesises on the possible structures of the gene regulatory network (GRN) underlying the biological model they study. It would be very helpful to be able to rank the hypothesises using existing data. Microarray technologies enable us to monitor the expression levels of tens of thousands of genes simultaneously. Given the expression levels of almost all of the well-substantiated genes in an organism under many experimental conditions, it is possible to evaluate the hypothetical gene regulatory networks with statistical methods. We present RankGRN, a web-based tool for ranking hypothetical gene regulatory networks. RankGRN scores the gene regulatory network models against microarray data using Bayesian Network methods. The score reflects how well a gene network model explains the microarray data. A posterior probability is calculated for each network based on the scores. The networks are then ranked by their posterior probabilities. RankGRN is available online at [http://GeneNet.org/bn]. RankGRN is a useful tool for evaluating the hypothetical gene network models’ capability of explaining the observational gene expression data (i.e. the microarray data). Users can select the gene network model that best explains the microarray data.