Research on Image Classification of Sports Training Video Based on Grey Relational Analysis and Support Vector Machine

In practical applications, the rational operation rule derivation can lead to significant improvements in the middle and long-term joint operation of cascade hydropower stations. The key issue of actual optimal operation is to select effective attributions from the deterministic optimal operation results, however, there is still no general and mature method to solve this problem. Firstly, the joint optimal operation model of hydropower reservoirs considering backwater effects are established. Then, the dynamic programming and progressive optimality algorithm are applied to solve the joint optimal operation model and the deterministic optimization results are obtained. Finally, the grey relational analysis method is applied to select more effective factors from the obtained results as the input of a support vector machine for further operation rule derivation. The Xi Luo-du and Xiang Jia-ba cascade reservoirs in the upper Yangtze river of China are selected as a case study. The results show that the proposed method can obtain better input factors to improve the performance of SVM, and smallest value of root mean square error by the proposed method of Xi Luo-du and Xiang Jia-ba are 94.33 and 21.32, respectively. The absolute error of hydropower generation for Xi Luo-du and Xiang Jia-ba are 2.57 and 0.42, respectively. Generally, this study provides a well and promising alternative tool to guide the joint operation of hydropower reservoir systems.

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AN IMPROVEMENT IN SUPPORT VECTOR MACHINE CLASSIFICATION MODEL USING GREY RELATIONAL ANALYSIS FOR CANCER DIAGNOSIS

Jurnal Teknologi ◽

10.11113/jt.v78.9548 ◽

2016 ◽

Vol 78 (8-2) ◽

Author(s):

Roselina Sallehuddin ◽

Sh Hafizah Sy Ahmad Ubaidillah ◽

Azlan Mohd Zain ◽

Razana Alwee ◽

Nor Haizan Mohamed Radzi

Keyword(s):

Support Vector Machine ◽

Cancer Diagnosis ◽

Grey Relational Analysis ◽

Classification Model ◽

Support Vector ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Relational Analysis ◽

Ranking Scheme ◽

Grey Relational

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48% by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97% by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis.

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Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

International Journal of Image and Graphics ◽

10.1142/s0219467820500357 ◽

2020 ◽

Vol 20 (04) ◽

pp. 2050035

Author(s):

Sumit Dhariwal ◽

Sellappan Palaniappan

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Error Rate ◽

Kernel Functions ◽

Support Vector ◽

Image Normalization ◽

Novel Approach ◽

Low Levels ◽

To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

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Grey-based multiple instance learning with multiple bag-representative

AI Communications ◽

10.3233/aic-200628 ◽

2020 ◽

Vol 33 (2) ◽

pp. 59-73

Author(s):

Lingyu Ren ◽

Youlong Yang ◽

Liqin Sun ◽

Xu Wu

Keyword(s):

Support Vector Machine ◽

Multiple Instance Learning ◽

Support Vector ◽

Standard Assumption ◽

Level Information ◽

Speed Up ◽

Instance Space ◽

Grey Relational ◽

Multiple Class

Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design efficient instance selection techniques to speed up the training process, without compromising the performance. Firstly, a multiple instance learning model of support vector machine based on grey relational analysis is proposed in this paper. The data size can be reduced, and the importance of instances in the bag can be preliminarily judged. Secondly, this paper introduces an algorithm with the bag-representative selector that trains the support vector machine based on bag-level information. Finally, this paper shows how to generalize the algorithm for binary multiple instance learning to multiple class tasks. The experimental study evaluates and compares the performance of our method against 8 state-of-the-art multiple instance methods over 10 datasets, and then demonstrates that the proposed approach is competitive with the state-of-art multiple instance learning methods.

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