A key feature extraction method based on improved minimum spanning tree for high-dimensional data
A key feature, high-dimensional data technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of high-dimensional sample data preprocessing, without considering the application background of the data set, and sample data cannot be data mined and analysis issues
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Embodiment 1
[0070] The key feature extraction method of high-dimensional data based on the improved minimum spanning tree includes the following steps:
[0071] Step 1. Preprocessing the hot-rolled strip data, including: data cleaning, data integration and discretization of continuous attributes.
[0072] 1. Data cleaning
[0073] For the abnormal values in the hot rolling process data, since each attribute in the data has its own reasonable range in the actual production process, the upper and lower limit search method is used to find the abnormal values. Not only set reasonable upper and lower limits for the value of each characteristic attribute according to the prior knowledge in the actual production process, but the data beyond this reasonable range are considered as outliers. Since the data samples containing missing values and outliers account for a very small proportion in the data set, and the data of each strip are independent of each other, the operation of directly delet...
Embodiment 2
[0136] Algorithm flow process of the present invention is as follows:
[0137] Step 1. Initialize, define input and output data sets, complete data cleaning, data integration and attribute discretization;
[0138] Step 2. Remove irrelevant attributes;
[0139] Step 3, constructing a minimum spanning tree;
[0140] Step 4. Complete the segmentation of the minimum spanning tree, and select key (representative) features based on the correlation measure between attributes.
[0141] First make the following variable assumptions: Let TR be the calculated feature attribute F i The intermediate variable of the symmetric uncertainty between the target attribute C and the target attribute C, FC is the calculation of two feature attributes F i with F j Intermediate variables with symmetric uncertainties between, for the subtree T i The feature attribute with the largest symmetric uncertainty value between the center and the target attribute C, k is the number of nodes in the conne...
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