Statistical Inference Through Variable Adaptive Threshold Algorithm in Over-Sampling the Imbalanced Data Distribution Problem

2022 ◽  
pp. 267-278
Author(s):  
S. Karthikeyan ◽  
T. Kathirvalavakumar
Keyword(s):  
Statistical Inference ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Adaptive Threshold ◽  
Distribution Problem ◽  
Threshold Algorithm

scholarly journals A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

2021 ◽  
Vol 11 (23) ◽  
pp. 11116
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Chunting Liu
Keyword(s):  
Fault Diagnosis ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Driven ◽  
Total Cost ◽  
Operation And Maintenance ◽  
Diagnosis Method ◽  
Overall Performance ◽  
Diagnosis Model ◽  
The Cost

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

Image Denoising with Contour-Based Directionlet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 3607-3611
Author(s):  
Huan An Xu ◽  
Guo Hua Peng ◽  
Zhe Liu
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Numerical Experiments ◽  
Adaptive Threshold ◽  
Perceptual Quality ◽  
Laplacian Pyramid ◽  
Low Frequencies ◽  
Threshold Algorithm ◽  
Image Transform

A novel mutiscale and directionally adaptive image transform called contour based directionlet tansform is presented. Directionlet transform (DT) has shown its charming performance in image processing, but it has scrambled frequencies. Laplacian Pyramid is employed here to separate the low frequencies before applying DT for avoiding the drawback. And an adaptive threshold algorithm is proposed for denoising. Numerical experiments are performed to assess the applicability of the proposed method. The obtained results show that the proposed scheme outperforms Wavelet and Directionlet transforms in terms of numerical and perceptual quality.

Classification with Local Clustering in Imbalanced Data Sets

2011 ◽  
Vol 219-220 ◽  
pp. 151-155 ◽  
Author(s):  
Hua Ji ◽  
Hua Xiang Zhang
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Local Clustering ◽  
Rare Class ◽  
Novel Method ◽  
The Cost

In many real-world domains, learning from imbalanced data sets is always confronted. Since the skewed class distribution brings the challenge for traditional classifiers because of much lower classification accuracy on rare classes, we propose the novel method on classification with local clustering based on the data distribution of the imbalanced data sets to solve this problem. At first, we divide the whole data set into several data groups based on the data distribution. Then we perform local clustering within each group both on the normal class and the disjointed rare class. For rare class, the subsequent over-sampling is employed according to the different rates. At last, we apply support vector machines (SVMS) for classification, by means of the traditional tactic of the cost matrix to enhance the classification accuracies. The experimental results on several UCI data sets show that this method can produces much higher prediction accuracies on the rare class than state-of-art methods.

A New Method of Line Feature Generalization Based on Shape Characteristic Analysis

2011 ◽  
Vol 18 (4) ◽  
pp. 597-606 ◽  
Author(s):  
Hongshan Nie ◽  
Zhijian Huang
Keyword(s):  
Adaptive Threshold ◽  
Experimental Results ◽  
New Method ◽  
Characteristic Analysis ◽  
Line Feature ◽  
Shape Characteristic ◽  
Position Accuracy ◽  
Threshold Algorithm ◽  
Key Points ◽  
Line Features

A New Method of Line Feature Generalization Based on Shape Characteristic Analysis This paper presents a piecewise line generalization algorithm (PG) based on shape characteristic analysis. An adaptive threshold algorithm is used to detect all corners, from which key points are selected. The line is divided into some segments by the key points and generalized piecewise with the Li-Openshaw algorithm. To analyze the performance, line features with different complexity are used. The experimental results compared with the DP algorithm and the Li-Openshaw algorithm show that the PG has better performance in keeping the shape characteristic with higher position accuracy.

scholarly journals QRS Detection Based on Improved Adaptive Threshold

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xuanyu Lu ◽  
Maolin Pan ◽  
Yang Yu
Keyword(s):  
High Speed ◽  
Detection Rate ◽  
Adaptive Threshold ◽  
Memory Storage ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Peak Finding ◽  
Threshold Algorithm ◽  
Complex Detection ◽  
Electrocardiogram Ecg

Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

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