Method and apparatus for nondestructive grouping of unordered categorical variable information

Abstract

The invention discloses a method and an apparatus for nondestructive grouping of unordered categorical variable information. The method comprises the steps of calculating an evidence weight value for the value of each category in unordered categorical variables under the supervision of a two-value target variable; and performing isobathic grouping on the evidence weight values, dividing the evidence weight values into M regions, and taking the M regions as groups of the unordered categorical variables. According to the method and the apparatus for the nondestructive grouping of the unordered categorical variable information, disclosed by the invention, the grouping process is simple and easy to understand, the calculation speed is high, and the distinguishing capability of the unordered categorical variables for the target variable can be well reserved.

Description

technical field [0001] The invention relates to the field of grouping of disordered classification variables, in particular to a method and device for grouping information of disordered classification variables without loss. Background technique [0002] With the development of technologies such as the Internet, cloud computing, and the Internet of Things, the amount of data in various industries has increased explosively. Among these data, disordered categorical variables account for a large part. For these disordered categorical variables, There needs to be a fast and effective method for data preprocessing, so as to quickly discover the value in the data. [0003] At present, for the problem of variable grouping in data preprocessing, most people study the grouping or binning of continuous variables. As for how to achieve effective information lossless grouping of unordered categorical variables, two processing methods are basically adopted: one is to group by experience...

AI Technical Summary

Problems solved by technology

As for how to achieve effective information lossless grouping of unordered categorical variables, two processing methods are basically adopted: one is to group by experience, which is extremely inefficient and cannot be guaranteed to be effective; Grouping is used directly. In this way, when the value distribution of unordered categorical variables is very wide, the effect in subsequent modeling and other applications is often very poor.

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