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Data set classification learning algorithm automatic selection system and method

A learning algorithm and automatic selection technology, applied in the field of machine learning, can solve problems such as excessive calculation load and incompatibility of selection methods, saving time and energy, and improving efficiency and accuracy.

Active Publication Date: 2020-05-29
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Aiming at the problem that the selection methods of learning algorithms involved in existing data processing are not universal, and the amount of calculation is too large if one tries one by one, the present invention provides a system and method for automatically selecting learning algorithms for data set classification

Method used

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  • Data set classification learning algorithm automatic selection system and method
  • Data set classification learning algorithm automatic selection system and method
  • Data set classification learning algorithm automatic selection system and method

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specific Embodiment approach 1

[0042] Specific implementation mode 1. Combination figure 1 As shown, the first aspect of the present invention provides a system for automatically selecting data set classification learning algorithms, including:

[0043] Training feature selection module 100: used to select each classification problem data set from the UCI machine learning database and Kaggle data set, process each classification problem data set, and obtain corresponding taxonomic meta-knowledge; at the same time, the knowledge base module obtains each classification problem The optimal algorithm number corresponding to the problem data set;

[0044] Selector module 200: used to use Bayesian optimization algorithm to select effective features from the classification meta-knowledge as meta-features; use all the meta-features and their corresponding optimal algorithm numbers to form a selector training set, and train meta-knowledge The selector is trained, and the trained meta-knowledge trains the selector t...

specific Embodiment approach 2

[0075] Embodiment 2. Another aspect of the present invention also provides a method for automatically selecting a data set classification learning algorithm, including:

[0076] Select each classification problem data set from the UCI machine learning database and Kaggle data set, process each classification problem data set, and obtain the corresponding taxonomic meta-knowledge; at the same time, obtain the optimal algorithm number corresponding to each classification problem data set from the knowledge base A step of;

[0077] Using the Bayesian optimization algorithm to select effective features from the classification meta-knowledge as meta-features; using all the meta-features and their corresponding optimal algorithm numbers to form a selector training set, and training the meta-knowledge selector; The trained meta-knowledge training selector obtains its optimal algorithm number for each meta-feature;

[0078] Process the data set to be processed to obtain meta-features...

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Abstract

The invention discloses a data set classification learning algorithm automatic selection system and method, and belongs to the technical field of machine learning. The method aims at solving the problems that a selection mode of a learning algorithm involved in existing data processing does not have universality, and if attempts are conducted one by one, the calculated amount is too large. The system comprises a training feature selection module for selecting each classification problem data set and processing each classification problem data set to obtain corresponding classification meta-knowledge; a selector module which is used for selecting effective features from the classified meta-knowledge as meta-features, forming a selector training set and training a meta-knowledge training selector; an algorithm selection module which is used for processing the to-be-processed data set to obtain to-be-processed meta-features, analyzing by adopting a meta-knowledge training selector to obtain an optimal learning algorithm of the to-be-processed data set; and a knowledge base module which is used for obtaining an algorithm selection training set comprising a one-to-one correspondence relationship between different classification problem data sets and corresponding learning algorithms. The method can predict the optimal learning algorithm for the data set.

Description

technical field [0001] The invention relates to a data set classification learning algorithm automatic selection system and method, and belongs to the technical field of machine learning. Background technique [0002] In recent years, the booming trend of machine learning technology has been particularly significant, constantly prompting industries such as IT Internet, finance, education, and medicine to subvert traditional operating methods and open up innovative AI development models. For example, the research and development of artificial intelligence chips, data mining and financial analysis, and even the introduction of AI services such as personalized medical solutions and intelligent assistants, it can be seen that the combination of machine learning technology and other fields can show huge application prospects and commercial value. [0003] In the field of machine learning, classification is the most basic and important research direction. Many other AI application...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 王宏志王春楠张天赐陈含笑
Owner HARBIN INST OF TECH
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