Integrated classifier and classification method thereof

A technology that integrates classifiers and classification methods, which is applied in the direction of machine executive devices, instruments, character and pattern recognition, etc., can solve the problems of slow speed and low precision, and achieve the effect of maintaining the integrity of decision-making

Inactive Publication Date: 2013-02-13
NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
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Problems solved by technology

[0009] In order to solve the problems of slow speed, low precision, attribute subsets with bias characteristics and attribute subsets being non-deterministic polynomials in

Method used

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  • Integrated classifier and classification method thereof
  • Integrated classifier and classification method thereof
  • Integrated classifier and classification method thereof

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Experimental program
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specific Embodiment approach 1

[0045] Specific implementation mode 1. Combination figure 1 and figure 2 Specifically illustrate this embodiment, the classification method of the integrated classifier described in this embodiment, it comprises the following steps:

[0046] Step 1. Read the raster data to be processed by means of a combination of multi-process and multi-thread. The specific process includes the following steps:

[0047] A. Input the number n of sub-classifiers of the integrated classifier;

[0048] n is the number of sub-classifiers, n is greater than or equal to 2, all the spatial attributes of the raster data are divided into n parts according to the decision-making ability through the expectation algorithm, each classifier has all the classification capabilities of the complete set,

[0049] B. Start n+1 processes;

[0050] Among them, the n+1 processes are Rank 0, Rank 1...Rankn; Rank0 is the management process, and Rank 1...Rankn is the operation process, and the operation process Ra...

specific Embodiment approach 2

[0059] Embodiment 2. The difference between this embodiment and the classification method with integrated classifiers described in Embodiment 1 is that the raster data described in step A is high-dimensional raster data.

[0060] In this embodiment, for massive high-dimensional raster data, traditional algorithms are slow in speed and low in precision. However, this patent achieves fast processing of raster data and acquisition of classification models. Moreover, due to the adoption of a heterogeneous decision-making mechanism, the classification accuracy is also high.

specific Embodiment approach 3

[0061] Specific embodiment three, combine image 3 This embodiment is specifically described. The difference between this embodiment and the classification method of the integrated classifier described in Embodiment 1 or Embodiment 2 is that each thread described in step 2 starts to discretize the raster data of the corresponding spatial continuous attribute The specific steps are:

[0062] Step 21. Set the number of clusters to ceil;

[0063] Step 22. Find the initial center of uniformly distributed clustering between the maximum value and the minimum value of the spatial continuity attribute started by the thread;

[0064] Step two and three, according to the K-Means algorithm, cluster the initial center of the evenly distributed cluster to form ceil clusters;

[0065] Step 24, output its minimum and maximum values ​​for each cluster to form ceil range intervals;

[0066] Step 25: Construct the ceil value range intervals into an interval list.

[0067] In this embodiment...

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Abstract

The invention relates to an integrated classifier and a classification method thereof, and the classifier and the method are used for solving the problems of low speed and precision as well as biasing characteristics and nondeterministic polynomial of attribute subsets in the field of spatial raster data monitoring and classification. The integrated classifier and the classification method adopt an attribute division mode, combine training data subsets with a parallel computing technique, and can be applied to high-latitude raster data; and as the integrated classifier and the classification method adopt a fuzzy rough sets theory as a standard for parallel division of high-altitude attributes, each subset has independent characteristics, and the integrity of a strategy is maintained. Therefore, the integrated classifier and the classification method are applicable to discrete and continuous heterogeneous data and can be applied to the fields of remote sensing and geographical information systems.

Description

technical field [0001] The invention relates to the fields of remote sensing and geographic information systems. Background technique [0002] In the field of supervised classification of existing spatial raster data, the main applied technologies include neural network, support vector machine, decision tree, Bayesian, KNN and other algorithms. The main method used by these algorithms is to input the training data algorithm to learn and generate a "classification model", through which the category information of the location data can be further predicted. For high-dimensional data, the "attribute selection" algorithm is usually used to reduce the dimension and improve the speed. [0003] Another important technology currently used is the "integrated classifier". The integrated classifier votes through a combination of heterogeneous multiple classifiers, and is expected to obtain higher classification accuracy than a single classifier. [0004] .In the process of processing...

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Application Information

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IPC IPC(8): G06K9/62G06F9/38
Inventor 张淑清潘欣张策姜春雷
Owner NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S
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