Random forest classification system based on kernel extreme learning machine and parallelization

Abstract

The invention discloses a random forest classification system based on a kernel extreme learning machine and parallelization, and the system comprises a single-unit module and a parallelization module. The single-unit module is mainly used for the processing of a non-mass data set, and comprises four submodules: a data extraction module, a model training module, a model evaluation model, and a model optimization module. According to the technical scheme of the invention, the system can support the processing of mass data, and also can achieve the classification performances of data.

Description

technical field [0001] The invention belongs to the field of computer software and relates to a random forest classification system based on kernel extreme learning machine and parallelization. Background technique [0002] With the development of artificial intelligence, machine learning technology is becoming more and more important as the core technology of artificial intelligence. Machine learning technology specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their own performance. After half a century of development, machine learning algorithms have emerged in an endless stream, from neural networks in the last century to deep learning that has developed rapidly in recent years, as well as algorithms such as support vector machines (SVM), decision trees, and naive Bayesian classifiers. Algorithms have a wide range of needs and ap...

AI Technical Summary

Problems solved by technology

Traditionally, a single-machine multi-thread method is used to parallelize the program. However, for massive data, the single-machine multi-thread method is still not feasible, and multiple machines need to be used for parallelization.

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