2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献_第2页

Explainable Machine Learning Models for Pneumonia Mortality Risk Prediction Using MIMIC-III Data 使用MIMIC-III数据预测肺炎死亡率风险的可解释机器学习模型

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068438

James Sanii, Wai-Yip Chan

{"title":"Explainable Machine Learning Models for Pneumonia Mortality Risk Prediction Using MIMIC-III Data","authors":"James Sanii, Wai-Yip Chan","doi":"10.1109/ISCMI56532.2022.10068438","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068438","url":null,"abstract":"To gain trust, machine learning (ML) models used in high stake applications such as clinical decision support need to provide explainable behaviours and outputs. To assess whether interpretable explanations can be obtained without sacrificing prediction performance, we compare using “black box” versus “glass box” models for predicting the mortality risk of patients diagnosed with pneumonia, using data in the MIMIC-III dataset. We examine five types of black box models: random forest (RF), support vector machine (SVM), gradient boosting classifier (GBC), AdaBoost (ADA), and multilayer perceptron (MLP), and three types of glassbox models: K-nearest neighbor (KNN), explainable boosting machine (EBM), and generalized additive models (GAM). When trained using 417 features, a black box RF model performs best with AUC of 0.896. With the feature set size reduced to 19, an EBM model performs the best with AUC 0.872. Both models exceed the AUC of 0.661, the best previously reported for the task. Our results suggest that ML models with inbuilt explainability may provide prediction power as attractive as black box models.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the performance of the Quantum Approximate Optimisation Algorithm to solve the Quadratic Assignment Problem 评价量子近似优化算法求解二次分配问题的性能

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068445

M. Khumalo, K. Prag, K. Nixon

{"title":"Evaluating the performance of the Quantum Approximate Optimisation Algorithm to solve the Quadratic Assignment Problem","authors":"M. Khumalo, K. Prag, K. Nixon","doi":"10.1109/ISCMI56532.2022.10068445","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068445","url":null,"abstract":"The performance of the Quantum Approximate Optimisation Algorithm (QAOA) in solving the Quadratic Assignment Problem (QAP) is evaluated, with the Variational Quantum Eigensolver (VQE) as a benchmark. The QAP is directly revelant to numerous industry scenarios. The QAP, a Combinatorial Optimisation Problem (COP), is classified as $mathcal{NP}$ -Hard. This classification means CPU time increases exponentially as the problem size scales when solving the QAP using deterministic optimisation techniques. Therefore, this work investigates the QAOA in search of a non-deterministic optimisation technique to efficiently obtain solutions to the QAP. This research compares two warm start techniques to solve QAP instances of sizes 3 to 7. The metrics of comparison - that measure efficiency and solution quality - were introduced in previous work on this topic. For the QAOA, the impact of the p-value, a determination of circuit depth, is investigated. Of the two quantum hybrid heuristics, the VQE retrieves solutions in a shorter computational time with a smaller circuit size, which allows for solving instances with a larger problem size. Compared to the VQE, the QAOA performs better in terms of feasibility as the problem size scales. The quantum warm start method results implies that the QAOA may not maintain higher solution quality for instances larger than size 4. Still, further investigation should be conducted once quantum devices with more qubits and higher quantum volumes are available.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"60 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128013900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Potential of a Genetic Algorithm on a Real-World Complex Scheduling Problem 探索遗传算法在现实世界复杂调度问题上的潜力

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068465

Szilvia Jáhn-Erdös, Bence Kövári

{"title":"Exploring the Potential of a Genetic Algorithm on a Real-World Complex Scheduling Problem","authors":"Szilvia Jáhn-Erdös, Bence Kövári","doi":"10.1109/ISCMI56532.2022.10068465","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068465","url":null,"abstract":"Genetic algorithms for NP-complete problems are widespread since it is easy to obtain a solution to the problem. However, its optimality gives the real issue and is not guaranteed to be achievable. In our research, we address a special subproblem of scheduling problems, the final exam scheduling, in which special requirements restrict the state space, which often contradicts each other. The task's difficulty is the massive size of the state space. Genetic algorithm-based solutions were considered since a MILP solver could not find a solution in a reasonable time. A model was built to solve this problem using the genetic algorithm. Most of the possibilities were seen in the different mutation procedures, so we investigated them in more detail. A question for genetic algorithms is what parameters and probabilities to run the model with since the more freedom we give to the run, the larger the runtime. Finding the threshold between the two is essential. Therefore, our experiments measured sizeable real data sets to find the optimal values for this complex problem. The resulting algorithm can significantly facilitate the lengthy manual scheduling processes carried out so far in our university.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128649661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks 基于多元LSTM神经网络的不可替代代币价格预测

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068442

Jerome Branny, Rolf Dornberger, T. Hanne

引用次数: 0

Loan Repayment Prediction Using Logistic Regression Ensemble Learning With Machine Learning Algorithms 使用逻辑回归集成学习与机器学习算法的贷款还款预测

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068483

T. Dinh, Binh Pham Thanh

{"title":"Loan Repayment Prediction Using Logistic Regression Ensemble Learning With Machine Learning Algorithms","authors":"T. Dinh, Binh Pham Thanh","doi":"10.1109/ISCMI56532.2022.10068483","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068483","url":null,"abstract":"Lending activities are an important part of the credit activities of financial institutions and banks. This is an area that brings great potential for development as well as a sustainable source of profit for financial institutions and banks. However, lending to customers also brings high risks. Therefore, predicting the ability to repay on time and understanding the factors affecting the repayment ability of customers is extremely important and necessary, to help financial institutions and banks enhance their ability to pay debts. customers' ability to identify and pay debts on time, contributing to minimizing bad debts and enhancing credit risk management. In this study, Machine Learning models will be used: Proposing a method to combine Logistic Regression with Random Forest, Logistic Regression with K-Nearest Neighbor, Logistic Regression with Support Vector Machine, Logistic Regression with Artificial Neural Network, Logistic Regression with Long short-term memory and finally Logistic Regression with Decision Tree to predict customers' ability to repay on time and compare and evaluate the performance of Machine Learning models. As a result, the Logistic Regression with the Random Forest model ensemble is found as the optimal predictive model and it is expected that Fico Score and annual income significantly influence the forecast.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133938416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Population-Based Algorithm for Solving the Fuzzy Capacitated Maximal Covering Location Problem 一种求解模糊最大覆盖定位问题的种群算法

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068459

Méziane Aïder, Imene Dey, M. Hifi

引用次数: 0

Hadamard Estimated Attention Transformer (HEAT): Fast Approximation of Dot Product Self-attention for Transformers Using Low-Rank Projection of Hadamard Product Hadamard估计注意变压器(HEAT):利用Hadamard积的低秩投影快速逼近变压器的点积自注意

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068484

Jasper Kyle Catapang

引用次数: 0

Comparison of Trajectory and Population-Based Algorithms for Optimizing Constrained Open-Pit Mining Problem 基于轨迹和种群的约束露天开采优化算法比较

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068481

I. Rahimi, Theodore Picard, Andrew Morabito, Kiriakos Pampalis, Aiden Abignano, A. Gandomi

引用次数: 0

CILDI: Class Incremental Learning with Distilled Images 用蒸馏图像进行课堂增量学习

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068463

Abel S. Zacarias, L. Alexandre

引用次数: 0

A Comparison of Linear Rank and Tournament for Parent Selection in a Genetic Algorithm Solving a Dynamic Travelling Salesman Problem 求解动态旅行商问题的遗传算法双亲选择的线性秩和比武比较

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068458

Ramona Boeh, T. Hanne, Rolf Dornberger

引用次数: 0