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Hybrid classification model is currently an active research area and successfully solves classification problems in credit scoring. Finding effective classificatory models is important. Classification in credit scoring has been regarded as a critical topic, with its related departments collecting huge amounts of data to avoid making the wrong decision. Filter feature selection model is important in...
This paper studies discriminant modeling method of compositional data. By adopting logratio transformation of compositional data and then implementing Fisher discriminant modeling method to the transformed data, the logcontrast linear discriminant function of compositional data is derived. The model presents the following advantages: i) the transformed data, which is scaled up to a broader range of...
Software metric models are useful in predicting the target software metric(s) for any future software project based on the project's predictor metric(s). Obviously, the construction of such a model makes use of a data sample of such metrics from analogous past projects. However, incomplete data often appear in such data samples. Worse still, the necessity to include a particular continuous predictor...
As for simulating Crown-Width Model, the traditional linear models often disregard the spatial component of data. However, estimating a local model reveals that the influence of the independent variables is inconstant over the whole study area. In this research, classic linear regression was reviewed, and Geographically Weighted Regression (GWR) method which belongs to local estimation was introduced...
By using orthogonal experiment design and the software Statistics Package for Social Science (in short, SPSS), in this paper, the effect of all the factors (dosage, concentration, time, mix ration of modification agent and temperature) is studied on the sedimentation volume of TiO2 powder, and the experiment data is processed through direct observation and analysis of variance. Moreover, based on...
Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) with multiple inputs and multiple outputs. Unascertained math is a useful tool for dealing with unascertained information. The unascertained theory was introduced to data envelopment analysis in this paper. A new model, which is called interval data envelopment analysis...
The accuracy and fairness of the assessment results to a large extent limited by the choice of methods of assessment data processing. In the current practice of the equipment support core competencies (ESCC) assessment, there are two methods of assessment data processing mainly used, which are named the simple arithmetic average method and the weighted arithmetic average method. In this paper, an...
A new approach to knowledge acquisition in interval-valued decision information system is proposed. In an interval-valued information system, by using the similarity degree of two different objects, a fuzzy similarity matrix, which generates a tolerance relation with a given level, is constructed. The universe of discourse is classified by the maximal tolerance classes based on the tolerance relation,...
The main considerations in the existing XML data dependency models are the structural relevancies of XML nodes but not the information relevancies in XML data which derive from the real world. In this paper, we propose an XML data dependency model (XDDM) based on entity segments. Our main objective is to make XML data dependencies in XDDM match the information relevancies derived from the real world.
Conventional gross error detection methods are mainly based on Gauss-Markov model and Least Squares Estimation, and are not adapted to gross error detection for control points of satellite remote sensing images, due to the serious ill-condition of satellite remote sensing imaging model and many iterations in the solving process. This paper proposed a method automatically detecting gross error of control...
Data mining technology is a useful tool for knowledge discovery from large-scale databases. At present, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. In this paper, we address this question and propose a domain-oriented data-driven knowledge acquisition model. A data-driven data mining...
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