Adjustable step length type multi-category integrated learning classification method
A classification method and integrated learning technology, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problems of high time cost, not accurate optimal value, etc., to reduce processing efficiency, improve classification prediction accuracy, The effect of improving generalization ability
Inactive Publication Date: 2015-04-29
SHANGHAI UNIV
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Problems solved by technology
However, even with line search, only an estimated optimal value for the step size is obtained, not an exact optimal value
In addition, the time cost of using line search calculations in each iteration is relatively large
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[0045] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.
[0046] refer to figure 1 , the present invention is a multi-category integrated learning classification method with shrinkable step size, taking the random data set generated by the Gaussian generation method as an example, the specific steps are as follows:
[0047] (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:
[0048] 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.
[0049] b) Preprocessing of the dataset to be classified. Each data form of...
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The invention relates to an adjustable step length type multi-category integrated learning classification 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; training M base classifiers according to the training data set sample weight and training step length; adjusting the step length as requirement; integrating all base classifies to obtain a final determining classifier; classifying the data set to be classified; storing the classifying result into a file to obtain classification predication reference. With the adoption of the method, the problems that the final classification interface is not optimized due to fixed step length and the classification predication precision is poor can be solved, and meanwhile, the time of line search estimation can be saved.
Description
technical field [0001] The invention relates to a multi-category integrated learning classification method with shrinkable step size. Background technique [0002] Ensemble learning has become an important research direction in machine learning. Because integrated learning has a certain theoretical basis and is simple to implement, it has higher prediction accuracy and the ability to resist "over-learning" than other classification methods, so 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 kno...
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Login to View More IPC IPC(8): G06F17/30G06K9/62G06K9/66
Inventor 吴悦严超
Owner SHANGHAI UNIV