Value Reduction Algorithm Based on Attribute Union

2014 ◽  
Vol 926-930 ◽  
pp. 3718-3721
Author(s):  
Yang Liu ◽  
Cong Hua Lan ◽  
Zhan Hong Tang
Keyword(s):  
Decision Rules ◽  
Extraction Methods ◽  
New Method ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Simple Operation ◽  
Rules Extraction ◽  
Attribute Value

The paper proposed a new algorithm of attribute value reduction use attribute union, only need to scan decision table one time, through simple operation can get all the concise rules. To avoid the decision information table for repeatedly and a large number of operations, algorithm is presented a new method of calculating rules metrics and rules extraction methods, it can not only get a concise decision rules, but also keep the accuracy of decision rules are the same. Example analysis proves the feasibility of the algorithm, and deal effectively with consistent decision table and inconsistent decision table, it can keep the decision table of information remains the same.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

Optimization Analysis on Dynamic Reduction Algorithm

2018 ◽  
Vol 6 (5) ◽  
pp. 447-458
Author(s):  
Yizhou Chen ◽  
Jiayang Wang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Reduction Process ◽  
New Method ◽  
Reduction Algorithm ◽  
Optimization Analysis ◽  
Sampling Process ◽  
Dynamic Methods ◽  
Definition Of

Abstract On the basis of rough set theory, the strengths of dynamic reduction are elaborated compared with traditional non-dynamic methods. A systematic concept of dynamic reduction from sampling process to the generation of the reduct set is presented. A new method of sampling is created to avoid the defects of being too subjective. And in order to deal with the over-sized time consuming problem in traditional dynamic reduction process, a quick algorithm is proposed within the constraint conditions. We have also proved that dynamic core possesses the essential characteristics of a reduction core on the basis of the formalized definition of the multi-layered dynamic core.

Extraction of Lipid-Soluble Marine Biotoxins

1995 ◽  
Vol 78 (2) ◽  
pp. 480-482 ◽  
Author(s):  
Donald J Hannah ◽  
Desmond G Till ◽  
Terry Deverall ◽  
Paul D Jones ◽  
Joanne M Fry
Keyword(s):  
New Zealand ◽  
Saline Solution ◽  
Extraction Methods ◽  
New Method ◽  
Mouse Bioassay ◽  
Shellfish Poisoning ◽  
Constant Weight ◽  
Lipophilic Compounds ◽  
Safe Method ◽  
Marine Biotoxins

Abstract A recent extensive outbreak of toxic shellfish poisoning (TSP) in New Zealand, with at least 4 types of toxicities present, required the development of a new method for detecting lipid-soluble marine biotoxins. The complexity of studying this outbreak, requiring large sample numbers, dictated the development of a robust and safe method for extracting lipid-soluble toxins. The new method is based on extraction of lipophilic compounds with acetone followed by partitioning into dichloromethane. The dichloromethane extract is evaporated to constant weight and suspended in a detergent–saline solution for use in a mouse bioassay. The new method produces an extract of superior quality, is quicker and more sensitive compared with extraction methods currently used.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

scholarly journals A New Method for Recording the Grain-Structure of Ice

1957 ◽  
Vol 3 (22) ◽  
pp. 131-132
Author(s):  
Keiji Higuchi
Keyword(s):  
Size Distribution ◽  
Replica Method ◽  
New Method ◽  
Grain Structure ◽  
Etch Pits ◽  
Etching Technique ◽  
Simple Operation ◽  
Individual Grains

AbstractSeparated etch pits can be produced by a simple operation using a plastic replica film. Combining this etching technique with Schaefer’s replica method, it is possible to record both the size distribution and the orientation of crystal axes of individual grains of ice.

Research of Improved Attribute Reduction Algorithm Based on Data Mining of Rough Set

2014 ◽  
Vol 644-650 ◽  
pp. 2120-2123 ◽  
Author(s):  
De Zhi An ◽  
Guang Li Wu ◽  
Jun Lu
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Reduction Algorithm ◽  
The Core ◽  
Rules Extraction

At present there are many data mining methods. This paper studies the application of rough set method in data mining, mainly on the application of attribute reduction algorithm based on rough set in the data mining rules extraction stage. Rough set in data mining is often used for reduction of knowledge, and thus for the rule extraction. Attribute reduction is one of the core research contents of rough set theory. In this paper, the traditional attribute reduction algorithm based on rough sets is studied and improved, and for large data sets of data mining, a new attribute reduction algorithm is proposed.

scholarly journals Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Sindhumol S. ◽  
Anil Kumar ◽  
Kannan Balakrishnan
Keyword(s):  
Independent Component Analysis ◽  
Brain Tissue ◽  
Brain Mri ◽  
Component Analysis ◽  
Extraction Methods ◽  
Clinical Analysis ◽  
Independent Component ◽  
New Method ◽  
Tissue Classification ◽  
Multispectral Data

Multispectral analysis is a potential approach in simultaneous analysis of brain MRI sequences. However, conventional classification methods often fail to yield consistent accuracy in tissue classification and abnormality extraction. Feature extraction methods like Independent Component Analysis (ICA) have been effectively used in recent studies to improve the results. However, these methods were inefficient in identifying less frequently occurred features like small lesions. A new method, Multisignal Wavelet Independent Component Analysis (MW-ICA), is proposed in this work to resolve this issue. First, we applied a multisignal wavelet analysis on input multispectral data. Then, reconstructed signals from detail coefficients were used in conjunction with original input signals to do ICA. Finally, Fuzzy C-Means (FCM) clustering was performed on generated results for visual and quantitative analysis. Reproducibility and accuracy of the classification results from proposed method were evaluated by synthetic and clinical abnormal data. To ensure the positive effect of the new method in classification, we carried out a detailed comparative analysis of reproduced tissues with those from conventional ICA. Reproduced small abnormalities were observed to give good accuracy/Tanimoto Index values, 98.69%/0.89, in clinical analysis. Experimental results recommend MW-ICA as a promising method for improved brain tissue classification.

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