Automated supervised learning method with multi-source data supported

Abstract

The invention discloses an automated supervised learning method with multi-source data supported, which comprises steps of (1) data pre-processing, (2) feature engineering, (3) model and super parameter regulation and (4) Bayesian pipeline optimization. The traditional data analysis process is automated, the process of manually regulating a machine learning pipeline is fundamentally improved, under the high coupling degree of super parameter regulation and pipeline optimization, the extensibility of the system on a supervised learning algorithm is greatly improved, a genetic algorithm is creatively put forward for regulation on the super parameters for the machine learning pipeline, and the efficiency for automated parameter regulation is greatly improved. Besides, a Bayesian optimizer is adopted for optimizing a pipeline algorithm combination, the problem of combination space explosion can be greatly solved, and finally, the accuracy and the efficiency of the automated supervised learning method are improved in the result.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to an automatic supervised learning method supporting multi-source data. Background technique [0002] Machine learning is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance. Machine learning is divided into supervised learning and unsupervised learning. . At present, the development of machine learning has entered a new stage, and the research fields have also been expanded unprecedentedly, including expert systems, cognitive simulation, planning and problem solving, data mining, network information services, image recognition, fault diagnosi...

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Problems solved by technology

[0004] At present, the development of automated machine learning is just in its infancy, and a large number of research topics are in the exploratory stage. The entire machine learning industry has not yet used automated machine learning to simplify the machine learning process. On the other hand, for data scientists, they have a lot of The repeated and boring tuning of parameters and the design of machine learning pipelines make them urgently need a tool that can ease their work. For clear and standardized data sets, they can simply design and adjust an optimal machine learning process

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