水库建设选址方案的决策分析

水库建设方案的评价,涉及投入和效益两方面。投入包括资金和工期等,称为“输入”:效益包括工农业用水增加、防洪能力强增等。称为“输出”。分析这种多输入,多输出的系统,应用数据包络分析方法是非常有效的。

电视广告作品评价的消费行为学研究

The quality of advertising copy is an important component of advertising service. An advertising system with only copy design and production but without evaluation is imperfect. Establishing an evaluation system of television advertising copy is the principal purpose of the present work. In terms of consumer behavior, the work focused on consumers' evaluation-in-general of T commercials. The research consisted of three subprograms. The first subprogrom was associated with the basic factors in the evaluation of television advertising copy. The second one was related to the relative importance of those basic factors. The last one was related to the way in which the consumers' synthetic evaluation of copy under multidimensions. These subprogram composed the evaluation system of television advertising copy. In the study of the first subprogram, by the use of a variaty of "multistage evaluation scale", a survey into consumers' evaluation-in-general of television ads was made, which obtained five factors, namely, credibility, attractiveness, suitability, cognition and affect impact, through factor analysis (Cum.Pct. = 56.2%, α = 0.84). The study of second subprogram acquired their relative weights by a popular method of weight in the area of decision analysis, the result was as followings: credibility-0.27, attractiveness-0.24, suitability-0.18, affect impact-0.16, cognition-0.15; and fanally, under the condition of quasi-experiment, the third studyestablished a mathematical model of the synthetic evaluation of television ad copy, which was expressed as O = ΣF * W, through a "synthetical" method of multidimensional decision making.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.