Data predicting model selecting device and method

Abstract

The invention provides a data predicting model selecting device and method. The model selecting device comprises a matrix decomposition unit and a model selecting unit, wherein the matrix decomposition unit is configured to perform singular value decomposition on a prediction matrix, the row / column vectors of the prediction matrix are sample vectors, the column / row vectors of the prediction matrix are feature vectors of the corresponding dimensions, and one of the sample vectors is a vector to be predicted, which contains a feature sequence to be predicted; the model selecting unit is configured to select a model to be applied to prediction of the feature sequence to be predicted on the basis of the submatrix of the sample vector, which is obtained through singular value decomposition and can reflect the prediction matrix. The data predicting model selecting device and method can reduce matrix computation dimensionality, reduce noise and achieve the effects of automatically and accurately selecting the prediction model.

Description

technical field [0001] The present disclosure generally relates to the field of data prediction, and in particular, relates to a model selection device and method for data prediction. Background technique [0002] In the field of data mining, for tasks such as prediction, classification, etc., there are many models available. For prediction tasks, for example, commonly available models include linear regression models, support vector regression models, and neural network-based models such as extreme learning machines. [0003] These models have their own applicable characteristics, and the effects are different for different data prediction tasks, even on different data sets. In general, certain models perform better than others on certain tasks and datasets. [0004] In order to achieve the best data prediction effect, it is often necessary to select an optional model in the early stage in order to use the best model that is most suitable for the current data set under th...

AI Technical Summary

Problems solved by technology

No matter which method is used, it depends on a certain degree of manual intervention, and often can only complete model selection through a small proportion of subset results on the complete data set, which has great limitations.

At the same time, the operation is cumbersome and time-consuming, but the effect may not be accurate enough

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