Quantum Fuzzy Support Vector Machine for Binary Classification

Huang, Xi; Zhang, Shibin; Chen, Lin; Xia, Jinyue

doi:10.32604/csse.2023.032190

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…The objective of Support Vector Machine Classification (SVM) [ 12 ] is to identify the optimal separating hyperplane that maximizes the margin of the training data. In cases where the training data is linearly separable, a linear classifier known as a hard margin support vector machine is learned by maximizing the margin.…”

Section: Concepts and Methodsmentioning

confidence: 99%

Comparative analysis of supervised learning algorithms for prediction of cardiovascular diseases

Dou,

Liu,

Meng

et al. 2024

THC

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BACKGROUND: With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction. OBJECTIVE: Cardiovascular disease (CVD) seriously jeopardizes human health worldwide, thereby needing the establishment of an effective CVD prediction model that can be of great significance for controlling the risk of the disease and safeguarding the physical and mental health of the population. METHODS: Considering the UCI heart disease dataset as an example, initially, a single machine learning prediction model was constructed. Subsequently, six methods such as Pearson, chi-squared, RFE and LightGBM were comprehensively used for the feature screening. On the basis of the base classifiers, Soft Voting fusion and Stacking fusion was carried out to build a prediction model for cardiovascular diseases, in order to realize an early warning and disease intervention for high-risk populations. To address the data imbalance problem, the SMOTE method was adopted to process the data set, and the prediction effect of the model was analyzed using multi-dimensional and multi-indicators. RESULTS: In the single classifier model, the MLP algorithm performed optimally on the preprocessed heart disease dataset. After feature selection, five features eliminated. The ENSEM_SV algorithm that combines the base classifiers to determine the prediction results by soft voting on the results of the classifiers achieved the optimal value on five metrics such as Accuracy, Jaccard_Score, Hamm_Loss, AUC, etc., and the AUC value reached 0.951. The RF, ET, GBDT, and LGB algorithms were employed in the first stage sub-model composed of base classifiers. The AB algorithm was selected as the second stage model, and the ensemble algorithm ENSEM_ST, obtained by Stacking fusion of the two stages exhibited the best performance on 7 indicators such as Accuracy, Sensitivity, F1_Score, Mathew_Corrcoef, etc., and the AUC reached 0.952. Furthermore, a comparison of the algorithms’ classification effects based on different training set occupancy was carried out. The results indicated that the prediction performance of both the fusion models was better than the single models, and the overall effect of ENSEM_ST fusion was stronger than the ENSEM_SV fusion. CONCLUSIONS: The fusion model established in this study improved the overall classification accuracy and stability of the model to a significant extent. It has a good application value in the predictive analysis of CVD diagnosis, and can provide a valuable reference in the disease diagnosis and intervention strategies.

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Section: Concepts and Methodsmentioning

confidence: 99%

Comparative analysis of supervised learning algorithms for prediction of cardiovascular diseases

Dou,

Liu,

Meng

et al. 2024

THC

View full text Add to dashboard Cite

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“…Te characteristics obtained by the decision tree model are easy to be afected by the amount of data. Te support vector machine is to fnd the support vector that can determine the optimal classifcation hyperplane from the training samples by maximizing the classifcation margin [25]. Te kernel function directly determines the performance of the support vector machine, but there is no suitable method to solve the problem of kernel function selection.…”

Section: Dictionary-based Acronym Disambiguationmentioning

confidence: 99%

ContextAD: Context-Aware Acronym Disambiguation with Siamese BERT Network

Ou,

Yao,

Luo

et al. 2023

International Journal of Intelligent Systems

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Acronym disambiguation is the process of determining the correct expansion of an acronym in given context, which can assist many downstream natural language processing tasks. Typically, existing methods on this task will directly perform semantic comparisons between the candidate expansions and the original sentence, ignoring the relevance of contextual information to expansions. To solve this issue, this paper proposes a context-aware acronym disambiguation method with Siamese BERT network (ContextAD). First, we combine each candidate expansion with corresponding acronym’s context to form a new sentence set. Then, the new and original sentences are input into a Siamese BERT network that can obtain the semantic similarity. The new sentences and the separate candidate expansions are input into the Siamese BERT network, respectively, along with the original sentences, which can obtain another semantic similarity. Finally, the two different semantic similarities are combined to determine the most suitable expansion. We quantify the improvement of our proposed ContextAD model against a state-of-the-art baseline using the public dataset of the shared tasks of acronym disambiguation (AD) held under AAAI-2021 workshop on SDU and show that it achieves a better performance based on the same BERT model.

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