Category weight combined integrated learning classifying method

A classification method and category weighting technology, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problems of category imbalance, not really considering the impact, not applicable to balanced data, etc., to reduce processing efficiency, The effect of improving classification prediction accuracy and improving generalization ability

Inactive Publication Date: 2015-04-29
SHANGHAI UNIV
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AI Technical Summary

Problems solved by technology

However, cost-sensitive ensemble learning methods are only suitable for unbalanced data, not balanced data
In addition, during model training, the influence of ea

Method used

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  • Category weight combined integrated learning classifying method
  • Category weight combined integrated learning classifying method
  • Category weight combined integrated learning classifying method

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Embodiment Construction

[0043] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] refer to figure 1 , the present invention is an integrated learning classification method combined with category weights, taking the random data set generated by the Gaussian generation method as an example, the specific steps are as follows:

[0045] (1) Preprocess the original data and convert it into a data format that can be processed by the classification method, such as figure 2 As shown, the specific steps are as follows:

[0046] a) Preprocessing of the training dataset. The preprocessing of the training data set is like this. Each piece of data must have fixed f attribute values, and a category attribute is added at the end, indicating that the category of this data is known. Therefore, there are f+1 attribute values ​​in total.

[0047] b) Preprocessing of the dataset to be classified. Each data form of the data...

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Abstract

The invention relates to a category weight combined integrated learning classifying method. The method comprises the steps of preprocessing original data; converting into data formats that can be processed by the classification method so as to obtain a training data set and a data set to be classified; initializing the training data set sample weight; re-iterating and training M base classifiers according to the training data set and the sample weight thereof; calculating the category weight; integrating all base classifies; classifying the data set to be classified through a determining classifier according to the category weight; storing the classifying result into a file to obtain classification predication reference. With the adoption of the method, the problem of unbalancing category training under multi-category and multi-classification condition of integrated learning can be solved, the excessive learning is inhibited well, and moreover, the model predication precision is improved, and reliable reference is provided for classification predication.

Description

technical field [0001] The invention relates to a method for classifying data of unknown categories, in particular to an integrated learning classification method combined with category weights. Background technique [0002] Ensemble learning has become an important research direction in machine learning. Because integrated learning has a certain theoretical basis, is simple to implement, and has good classification and prediction accuracy, it has been widely recognized and applied. As technology advances make data collection easier and easier, it is becoming more common to use ensemble learning to classify multiple categories of data. [0003] The use of integrated learning classification is to use a series of base classifiers for learning, and use some rules to integrate the results of these base classifiers, so as to obtain an integrated classifier with better learning effect and generalization ability than these base classifiers. When the number of categories is known,...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62G06K9/66
Inventor 吴悦严超
Owner SHANGHAI UNIV
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