Method for early classifying imbalance multi-variable time sequence data

A time-series, multi-variable technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as exploration, unbalanced time-series data classification and prediction in advance, achieve good early stage, strengthen interpretability, Effects on Solving Classification Problems

Inactive Publication Date: 2015-07-29
WUHAN UNIV
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[0009] However, the advance prediction of imbalanced time seri...

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  • Method for early classifying imbalance multi-variable time sequence data
  • Method for early classifying imbalance multi-variable time sequence data
  • Method for early classifying imbalance multi-variable time sequence data

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[0039] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0040] Aiming at the classification problem of unbalanced multivariate time series in early forecasting, the present invention proposes an effective method for constructing sub-classifiers by combining under-sampling and variable subspaces, and then integrating the sub-classifiers. By under-sampling the large category data in the training set, combining with the small category data to form multiple sub-training sets, extracting and selecting the kernel features of the variables in the sub-training set in the random variable subspace, and constructing rules bas...

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Abstract

The invention discloses a method for early classifying imbalance multi-variable time sequence data. The method comprises the following steps: carrying out the under-sampling for a large category of category data sets according to an imbalance ratio for the inter-category data scale imbalance problem into a plurality of subsets, and combining the subsets with a small category of category data to form a plurality of training subsets; extracting and selecting core features of each training subset, and establishing a sub-classifier based on a rule according to the core features, wherein the feature selection process is realized in a clustering way in order to solve the imbalance problem of the data scale of the intra-category sub-concepts so as to guarantee the diversity of the core features; and solving the weight of a classifying effect of data in the training set by utilizing the sub-classifier on the basis of each sub-classifier, and establishing an integrated classifier. The classifier can solve the multi-variable time sequence classifying problem of the imbalance data set, the accuracy is relatively high, and the earliness degree is good.

Description

technical field [0001] The invention belongs to the technical field of time series data mining, and in particular relates to a method for early classification of unbalanced multivariate time series data. Background technique [0002] In recent years, in the field of time series mining, the multivariate time series data classification problem has become a hot topic, and it is widely used in multimedia, medicine, manufacturing industry, financial applications and other fields. Since the time series itself is time-sensitive, it is particularly important to predict the category of time series data in advance, and it plays a very important role in the fields of medicine, industry, commerce and military. For example, in the analysis of certain diseases in medicine, if the abnormalities are judged as early as possible in the process of monitoring time series data such as electrocardiograms and electroencephalograms, early diagnosis and effective treatment of related diseases can be...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 何国良段勇李元香周国富
Owner WUHAN UNIV
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