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Classifier integration method for machine learning

An integrated method, machine learning technology, applied to instruments, computer components, character and pattern recognition, etc.

Inactive Publication Date: 2014-03-12
TIANJIN POLYTECHNIC UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The focus of the present invention is to solve the problem of how to use the difference of classifiers in the field of machine learning to design a classifier integration algorithm with better performance

Method used

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  • Classifier integration method for machine learning
  • Classifier integration method for machine learning
  • Classifier integration method for machine learning

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Experimental program
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Embodiment 1

[0029] A classifier integration method in machine learning, the classifier integration method includes the generation of a base classifier, giving the base classifier an optimized weight, and adopting a weighted voting method to classify data; using the RandomForest algorithm to generate Different multiple decision tree classifiers are used as the base classifier of the new algorithm, and the weight of the base classifier is optimized by using the L1_Magic algorithm to make full use of the differences between the base classifiers, so that the performance of the integrated classifier is better. Include the following steps:

[0030] (1) The first step: split the given data sample set; randomly divide the given data set containing N samples into two parts according to the split ratio of 9:1, and use them as training set and test set respectively. The sample numbers are marked as and ;

[0031] (2) The second step: learn the model on the training set to obtain different class...

Embodiment 2

[0035] This invention is a new classifier integration method applied in machine learning. The following is an application case of it.

[0036] Predicting genes from DNA sequences is an important topic in biology. Genes are some DNA segments, but there are usually some redundant DNA segments between genes. Predicting gene fragments is equivalent to predicting the boundary between genes and nongenes. A non-gene segment is connected behind a gene, such a boundary is represented by EI; and a gene is connected behind a non-gene, such a boundary is represented by IE; the rest of non-border DNA bases are represented by N. In this way, for a DNA sequence with unknown information, we can use the integrated classification method proposed in this paper to predict which are the boundaries of genes and non-genes, so as to infer which DNA fragments may be genes.

[0037] The ensemble method proposed in this paper is tested with traditional voting methods on a set of 3190 DNA sequences. C...

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Abstract

The invention provides a classifier integration method for machine learning. Basic classifiers generated by a RandomForest algorithm are used as the basic classifiers of a new algorithm. An L1_Magic algorithm is used to optimize the weights of the original classifiers, differences of the classifiers are fully utilized to allow the possibility of right classification of each sample in a training set to be consistent. By the voting manner with weights of the new integrated classifier, the accuracy of training set classification is increased. Compared with the RandomForest algorithm, the method has the advantages that the differences of the classifiers are fully utilized to allow the sample data classification accuracy of the integrated classifier to be increased. In addition, the problem of how to use diversity / difference to increase the effect of the integrated classifier in the field of artificial intelligence is solved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and pattern recognition, and is a novel classifier integration method applied in machine learning. Background technique [0002] Classification is a very important task in the field of artificial intelligence and pattern recognition. The purpose of classification is to learn a classification function or classification model (also often called a classifier), which can map data items in the database to one of the specified categories. Classification has a wide range of applications, such as medical diagnosis, fraud detection, credit grading for credit card systems, image pattern recognition, and more. Take the credit rating of the credit card system as an example to illustrate the application of classification. When the bank needs to predict the user's credit rating based on the user's characteristic information (such as age, occupation, income, education background, etc.), it can u...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 陈科朱波
Owner TIANJIN POLYTECHNIC UNIV
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