Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model
Abstract
:1. Introduction
1.1. Background
1.2. Related Work
1.3. Proposed Framework
2. Materials and Methods
2.1. Data Pre-Processing
2.2. Features Fusion
2.3. Multi-Kernel SVM-Apriori Model Construction
Algorithm 1. The algorithm process of the Multi-Kernel SVM–Apriori Model |
Input: experimental dataset {X,Y}; X is input, Y is the corresponding label (1 or −1) d, , Output: Multi-Kernel SVM–Apriori Model |
1: Initialize {X, Y}, w1, w2, w3, minsupport {X, Y} is experimental dataset, w1, w2, w3 are the weights of three kernels Equation (7), d is the degree of a polynomial (3), is the width of the Gaussian kernel (0.005), is the minimum support for generating frequent itemsets Equation (9). 2: Randomly select a subset of features as {Features}trak 3: Input {Features}trak according to 7:3 training, partitioning the {X, Y} into {X, Y}train, {X, Y}valid 4: Calculate predicted values: ypred = sign (w’ × x + b) Output: classifier: Acc = Σ (ypred == y)/length(y) 5: Input set {X, Y} to Multi-kernel SVM to obtain the Acc of all individual features and obtain the frequent itemset L1 which satisfies Acc > 6: For each k starting from k = 2: Repeat 7: Generate candidate feature set Ck by connecting frequent itemsets L (k − 1) – For each candidate feature set c in Ck: – For each transaction t in dataset S: – Check if c is a subset of t, and if so, increase the count of c – Calculate Acc for each candidate feature set based on Ck Update = mean(ΣACC L(k − 1)) Filter to obtain frequent feature set Lk which satisfies Acc > Until more frequent feature sets cannot be generated, return the frequent feature set column table L 8: Finally, perform Leave-One-Out Cross-Validation for each L (k − 1) and select the feature set with the highest ACC. |
2.4. Model Comparison
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Subjects | HC | EMCI | LMCI | AD | P |
---|---|---|---|---|---|
Number | 42 | 31 | 24 | 24 | - |
Gender (M/F) | 19/23 | 12/19 | 14/10 | 8/16 | <0.001 |
Age (Mean ± sd) | 74.2 ± 6.1 | 72.8 ± 6.4 | 70.9 ± 8.3 | 72.0 ± 7.6 | <0.001 |
EDU (Mean ± sd) | 16.5 ± 2.7 | 15.8 ± 2.7 | 16.8 ± 2.6 | 15.5 ± 2.9 | <0.001 |
L1 | L2 | L3 | L4 | … | Ln | |
---|---|---|---|---|---|---|
Generate From | S | L1 | L2 | L3 | … | Ln−1 |
Accuracy Threshold | 0.8 | mean(ACCL1) | mean(ACCL2) | mean(ACCL3) | … | mean(ACCLn−1) |
Model | Accuracy | F1 Score | Recall | Precision |
---|---|---|---|---|
LINEAR | 76.25% ± 2.64% | 74.8% ± 4.64% | 79% ± 6.46% | 71.8% ± 6.94% |
POLY | 76.88% ± 3.02% | 74.8% ± 3.91% | 79.6% ± 5.52% | 71.6% ± 7.52% |
RBF | 78.13% ± 3.29% | 77.6% ± 4.4% | 81.8% ± 6.43% | 74.8% ± 6.96% |
LINEAR-POLY | 76.25% ± 2.64% | 75.6% ± 4.45% | 81.9% ± 6.82% | 71% ± 6.8% |
LINEAR-RBF | 76.88% ± 3.02% | 74.9% ± 4.15% | 77.3% ± 5.76% | 73% ± 4.03% |
POLY-RBF | 76.88% ± 3.02% | 76.5% ± 3.37% | 76.8% ± 4.61% | 76.4% ± 3.75% |
MULTI-KERNEL | 76.88% ± 4.22% | 76.5% ± 4.53% | 79.9% ± 6.12% | 74.2% ± 5.07% |
OUR MODEL | 90.20% ± 1.54% | 93.20% ± 1.08% | 93.30% ± 1.19% | 93.50% ± 1.02% |
CART | 84.44% ± 2.34% | 82.2% ± 3.46% | 82% ± 5.03% | 83% ± 5.08% |
RANDOM FOREST | 84.44% ± 2.34% | 81.8% ± 3.79% | 83.3% ± 4.45% | 80.8% ± 4.92% |
BAYES | 85% ± 2.68% | 84.9% ± 2.92% | 85.2% ± 2.82% | 84.9% ± 3.07% |
BPNN | 72.94% ± 4.11% | 76.5% ± 3.72% | 82.2% ± 3.71% | 71.4% ± 4.2% |
PNN | 82.94% ± 1.86% | 84.1% ± 1.91% | 86.6% ± 2.27% | 81.8% ± 1.87% |
APRIORI+LINEAR | 86.6% ± 3.47% | 90.6% ± 2.41% | 91.5% ± 2.17% | 90.4% ± 2.46% |
APRIORI+POLY | 83.4% ± 3.69% | 88.7% ± 2.45% | 88.9% ± 2.64% | 89% ± 2.49% |
APRIORI+RBF | 87.8% ± 3.19% | 91.3% ± 2.16% | 92.1% ± 2.08% | 91.3% ± 2.16% |
APRIORI+LINEAR-POLY | 82.1% ± 4.79% | 85.5% ± 4.33% | 85.5% ± 4.14% | 85.7% ± 4.3% |
APRIORI+LINEAR-RBF | 88.6% ± 2.84% | 89.1% ± 2.56% | 89.9% ± 2.51% | 88.9% ± 2.81% |
APRIORI+POLY-RBF | 82% ± 3.74% | 87.7% ± 2.71% | 88.2% ± 2.57% | 87.6% ± 2.59% |
APRIORI+DT | 94.57% ± 0.78% | 95.6% ± 0.52% | 95.6% ± 0.52% | 96.5% ± 0.53% |
CNN(Overfitting) | 68.03% ± 1.33% | 26% ± 0% | 18% ± 0% | 50% ± 0% |
FCNN(Overfitting) | 73.09% ± 1.15% | 31% ± 0% | 22% ± 0% | 50% ± 0% |
Group | Accuracy | F1 Score | Recall | Precision |
---|---|---|---|---|
EMCI-HC | 81.88% ± 1.02% | 83.30% ± 0.78% | 83.60% ± 0.80% | 83.30% ± 0.78% |
LMCI-HC | 88.97% ± 0.97% | 91.60% ± 0.80% | 92.80% ± 0.87% | 90.80% ± 0.60% |
AD-HC | 89.73% ± 0.98% | 92.20% ± 0.75% | 94.00% ± 0.63% | 91.10% ± 0.83% |
AD-EMCI | 90.20% ± 1.54% | 93.20% ± 1.08% | 93.30% ± 1.19% | 93.50% ± 1.02% |
AD-LMCI | 92.13% ± 1.27% | 94.70% ± 1.00% | 94.70% ± 1.00% | 94.70% ± 1.00% |
Brain Region | AD-EMCI | AD-HC |
---|---|---|
Left hippocampus | 0.178128395 | 0.001062739 |
Right hippocampus | 0.13044139 | 0.055262965 |
Left parahippocampal gyrus | 0.080443747 | 0.034482065 |
Right parahippocampal gyrus | 0.045045533 | 0.008222377 |
Left amydala | 0.11497812 | 0.044599828 |
Right amydala | 0.089006046 | 0.117985895 |
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Hu, Z.; Tang, C.; Liang, Y.; Chang, S.; Ni, X.; Xiao, S.; Meng, X.; He, B.; Liu, W. Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model. Mathematics 2024, 12, 684. https://doi.org/10.3390/math12050684
Hu Z, Tang C, Liang Y, Chang S, Ni X, Xiao S, Meng X, He B, Liu W. Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model. Mathematics. 2024; 12(5):684. https://doi.org/10.3390/math12050684
Chicago/Turabian StyleHu, Zhixi, Congye Tang, Yingxia Liang, Senhao Chang, Xinyue Ni, Shasha Xiao, Xianglian Meng, Bing He, and Wenjie Liu. 2024. "Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model" Mathematics 12, no. 5: 684. https://doi.org/10.3390/math12050684
APA StyleHu, Z., Tang, C., Liang, Y., Chang, S., Ni, X., Xiao, S., Meng, X., He, B., & Liu, W. (2024). Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model. Mathematics, 12(5), 684. https://doi.org/10.3390/math12050684