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Method for handling missing values during data stream decision tree classification

A decision tree classification and processing method technology, applied in the field of missing value processing in the data flow decision tree classification, can solve the problems of reduced transmission efficiency, affecting the time performance of the ARC method, and time performance degradation

Inactive Publication Date: 2014-09-10
INST OF SOFTWARE - CHINESE ACAD OF SCI
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Problems solved by technology

[0005] However, the time performance of the ARC method decreases significantly when there are many characteristic attributes of the data samples, and the time performance is an important measurement index in data stream mining, so it seriously affects the time performance of the ARC method, reduces the transmission efficiency, and affects the actual application value

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  • Method for handling missing values during data stream decision tree classification
  • Method for handling missing values during data stream decision tree classification
  • Method for handling missing values during data stream decision tree classification

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Embodiment Construction

[0035] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0036] The main flow chart of the inventive method is as figure 1 Shown:

[0037] (1) Adaptive selection and establishment of missing processor

[0038] The specific process of adaptively selecting and establishing the missing processor is as follows: figure 2 As shown, the steps are:

[0039]Step 1: Detect attribute X in the current data sample i There are missing values;

[0040] Step 2: Read all samples of the same type as the current data sample in the sliding window W, and calculate the attribute X in the same type of samples i The standard deviation σ(X i );

[0041] Step 3: Preset σ m is the maximum acceptable sample standard deviation, if σ(X i ) does not exceed the threshold σ m , then go to step 4, otherwise go to step 5;

[0042] Step 4: Choose the mean substitution method to establish the missing processo...

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Abstract

The invention belongs to the technical field of data stream mining, and particularly relates to a method for handling missing values during data stream decision tree classification. The method includes reading data samples in data streams and updating sliding windows; updating missing handlers if the missing handlers corresponding to attributes are available when the detected attributes in the current data samples have the missing values, or adaptively selecting and creating missing handlers according to characteristics of data if the missing handlers corresponding to the attributes are not available; supplementing the missing values in the data samples by the aid of the missing handlers to obtain complete data samples, training the complete data samples according to a Hoeffding decision tree classification process and returning data stream decision tree classification results. Compared with existing methods, the method has the advantages that the method is superior in time performance, the classification accuracy of decision tree models can be sufficiently guaranteed, accordingly, the time expenditure can be reduced, the time performance can be improved, the data stream classification handling speeds can be increased, and requirements of actual data stream handling application can be met.

Description

technical field [0001] The invention belongs to the technical field of data stream mining, and in particular relates to a method for processing missing values ​​in data stream decision tree classification. Background technique [0002] With the advent of the era of big data, application systems generate data streams at high speed and continuously. How to mine useful information from data streams has become a hot spot for technicians. Data stream decision tree classification technology is an important research direction in data stream mining. This technology can be applied to many aspects such as network intrusion detection and credit card fraud. The actual data flow will have missing values ​​due to network transmission failures, sensor failures, or manual operation errors. In data stream decision tree classification, missing values ​​in the data stream can have a serious impact on classification accuracy. However, the data stream can only be scanned once during the mining...

Claims

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Application Information

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IPC IPC(8): G06F9/44
Inventor 吕品侯旭珊
Owner INST OF SOFTWARE - CHINESE ACAD OF SCI
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