34 results about "Categorical variable" patented technology

The present invention relates among others to a method of pooling samples to be analyzed for a categorical variable, wherein the analysis involves a quantitative measurement of an analyte, said method of pooling samples comprising providing a pool of n samples wherein the amount of individual samples in the pool is such that the analytes in the samples are present in a molar ratio of x0:x1:x2:x(n−1), and wherein x is equal to a positive value other than 1 representing the pooling factor.

Dimensional data with attributed categorical variables is mined against a continuous target with any data mining method by ranking variables. The ranked variables are used to generate a tree. A population and a target value, obtained from a top node of the tree, are stored. The top node is removed from the tree to create a new tree with a next top node. Obtaining and storing a next population and a next target value for the next top node, and removing the top node or top field to create a new tree, are repeated. The listing of sequential top node parameters is plotted on a tree cusp curve that provides a graphical user interface enabling identification of a field which affect a greatest or a least number of records, based upon a magnitude of departure of the field from a norm.

The embodiment of the invention provides a mobile terminal which comprises a variable judgment module, a user class recognition module, an interface style management module and a display module, wherein the variable judgment module is used for judging whether a user class variable is valid; the user class recognition module is used for providing a user class setting interface through which a user can input user class information, and judging the class of the user in accordance with the user class information; the interface style management module is used for providing interface parameters corresponding to the class of the user in accordance with the class of the user; and the display module is used for displaying a user interface in accordance with the interface parameters provided by the interface style management module. The embodiment of the invention also provides a mobile terminal user interface management method. By using the mobile terminal and the user interface management method thereof, the user can be automatically switched to the style and settings matched with the user group in accordance with the user class information, thereby achieving better user experience.

Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.

The invention discloses a method and a system for optimizing mobile service resources of an operator. The method includes: counting the historical dialing data of the operator's customers, and the dialing data is a continuous variable; converting the continuous variable into a discrete characteristic variable through chi-square analysis; taking whether the customer has opened a mobile service as a binary classification variable, establishing the characteristic variable and classification Variable C4.5 decision tree model, wherein, in the decision tree model, calculate the information gain rate corresponding to each segmentation, and select the segmentation threshold with the largest information gain rate as the optimal segmentation threshold for this attribute; according to the decision The tree model calculates the value of the classification variable to obtain the prediction result of whether the customer subscribes to the mobile service; optimizes the mobile service resources of the operator according to the prediction result. Through the technical solution provided by the invention, the customer's demand for mobile service can be efficiently obtained from the current customer's dialing behavior, so as to realize the optimized deployment of the operator's mobile service resources.

The invention discloses a power transformer defect data mining method. The method includes the following steps that: defect attribute screening is performed on the historical defect data set D0 of a power transformer, so that a defect data set D1 can be formed; filling or deletion is performed on defect attributes in the D1, so that noise data can be decreased; new attributes are constructed based on existing attributes of the D1, discretization is performed on continuously-valued attributes, reasonable stratification is performed on categorical attributes, and therefore, a defect data set D2 can be formed; the correlation between input attributes and target attributes is calculated, uncorrelated attributes are deleted, the remaining attributes form a defect data set D3; the association relationships between the attributes of the defect data set are calculated by using an Apriori algorithm; and effective association rules are extracted, the defect factors of the power transformer are analyzed, an association rule knowledge base can be formed. With the power transformer defect data mining method of the invention adopted, the defects of the power transformer can be mined in a multi-dimensional and multi-level manner, the association relationships between the attributes can be extracted conveniently and fast, a basis can be provided for power transformer condition evaluation, and the accuracy of condition evaluation can be improved.

The invention provides a real-time prediction method of urban road traffic accident risk, which calculates by extracting the geometric linear data of each observation object in the observation set, the basic data of historical traffic flow n minutes before the occurrence of the traffic accident, and the historical weather condition data to obtain traffic The characteristic parameters of traffic flow n minutes before the accident and weather condition data are transformed into grades of categorical variables and the distribution probability of this grade, and then a real-time prediction model of urban road traffic accidents based on Poisson distribution is established, using the determined traffic flow characteristic parameters The level of weather condition data and the distribution probability of this level are used to calibrate the prediction model. When predicting the traffic accident risk of the required prediction object, it is only necessary to calculate the real-time traffic flow characteristic parameters and weather conditions of the required prediction object in real time. The level after the data is converted into a categorical variable and the distribution probability of the level can be used to predict the traffic accident risk of the desired prediction object using the calibrated formula.

The invention relates to a method for detecting urea-doped milk based on synchronous-asynchronous two-dimensional near-infrared related spectra. The method comprises the following steps: 1, preparing pure milk for experiments and urea-doped milk; 2, respectively scanning the near-infrared spectra of the pure milk for experiments and the urea-doped milk; 3, calculating to obtain the normalization synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the pure milk for experiments and the normalization synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the urea-doped milk; 4, building a discrimination model with a categorical variable matrix by a multi-dimensional partial least squares; 5, scanning and calculating unknown sample milk to obtain the synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the unknown sample milk, and substituting into the discrimination model to obtain whether urea is doped or not. Similarity and difference information of a to-be-analyzed system, changing with external interference, are fully utilized, and the influence of the single adoption of synchronous spectrum or asynchronous spectrum matrix redundant information on the model is overcome. The method is simple and scientific, and the analysis efficiency and the discriminating accuracy are high.