Soft Computing最新文献_第9页

Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation 利用支持向量机进行类平衡学习的方法：综述与经验评估

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-25 DOI: 10.1007/s00500-024-09931-5

Salim Rezvani, Farhad Pourpanah, Chee Peng Lim, Q. M. Jonathan Wu

引用次数: 0

A new approach for attribute reduction from decision table based on intuitionistic fuzzy topology 基于直觉模糊拓扑的决策表属性还原新方法

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09910-w

Tran Thanh Dai, Nguyen Long Giang, Vu Duc Thi, Tran Thi Ngan, Hoang Thi Minh Chau, Le Hoang Son

{"title":"A new approach for attribute reduction from decision table based on intuitionistic fuzzy topology","authors":"Tran Thanh Dai, Nguyen Long Giang, Vu Duc Thi, Tran Thi Ngan, Hoang Thi Minh Chau, Le Hoang Son","doi":"10.1007/s00500-024-09910-w","DOIUrl":"https://doi.org/10.1007/s00500-024-09910-w","url":null,"abstract":"Most of the current attribute reduction methods use the measure to define the reduct, such as the positive region of rough set theory (RS), information entropy, and distance. However, the size of the reduct based on the measures is still limited. To cope with this problem, we propose a new approach of attribute reduction based on using the intuitionistic fuzzy topology (IFT). Firstly, a new IFT structure based on the pre-order relation and the intuitionistic fuzzy base (IF-base) structure is introduced. Secondly, a new measure is proposed to evaluate the significance of the attribute based on the IF subbase. Finally, the new reduction algorithms based on the IF-base filter and filter-wrapper methods are presented. The theoretical and experimental results show that the proposed method is efficient in terms of size and accuracy of the reduct. Specifically, the reduct of the F_IFT algorithm has an average size of 50% smaller, and the FW_IFT algorithm has an average accuracy of 10% greater than those of the related algorithms. Significantly, the algorithm FW_IFT can very remove noisy attributes. The classification accuracy of the reduct is 15% higher than that of the original set of attributes.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"63 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAMME.C2 algorithm for imbalanced multi-class classification 用于不平衡多类分类的 SAMME.C2 算法

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09847-0

Banghee So, Emiliano A. Valdez

{"title":"SAMME.C2 algorithm for imbalanced multi-class classification","authors":"Banghee So, Emiliano A. Valdez","doi":"10.1007/s00500-024-09847-0","DOIUrl":"https://doi.org/10.1007/s00500-024-09847-0","url":null,"abstract":"Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. Real-world classification problems with severely imbalanced class distributions have increased substantially in recent years. In such cases, significantly fewer observations are available for minority classes to learn from than for majority classes. Despite this sparsity, the minority class is often considered as the more interesting class, yet the development of a scientific learning algorithm that is suitable for these observations presents numerous challenges. In this study, we further explore the merits of an effective multi-class classification algorithm known as SAMME.C2 that is specialized for handling severely imbalanced classes. This innovative method blends the flexible mechanics of the boosting techniques from the SAMME algorithm, which is a multi-class classifier, and the Ada.C2 algorithm, which is a cost-sensitive binary classifier that is designed to address highly imbalanced classes. We establish a scientific and statistical formulation of the SAMME.C2 algorithm, together with providing and explaining the resulting procedure. We demonstrate the consistently superior performance of this algorithm through numerical experiments as well as empirical studies.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"51 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-objective optimization approach for overlapping dynamic community detection 重叠动态群落检测的多目标优化方法

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09895-6

Sondos Bahadori, Mansooreh Mirzaie, Maryam Nooraei Abadeh

引用次数: 0

Inference on process capability index $$S_{pmk}$$ for a new lifetime distribution 针对新寿命分布的过程能力指数 $$S_{pmk}$$ 的推论

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09892-9

Kadir Karakaya

{"title":"Inference on process capability index $$S_{pmk}$$ for a new lifetime distribution","authors":"Kadir Karakaya","doi":"10.1007/s00500-024-09892-9","DOIUrl":"https://doi.org/10.1007/s00500-024-09892-9","url":null,"abstract":"In various applied disciplines, the modeling of continuous data often requires the use of flexible continuous distributions. Meeting this demand calls for the introduction of new continuous distributions that possess desirable characteristics. This paper introduces a new continuous distribution. Several estimators for estimating the unknown parameters of the new distribution are discussed and their efficiency is assessed through Monte Carlo simulations. Furthermore, the process capability index (S_{pmk}) is examined when the underlying distribution is the proposed distribution. The maximum likelihood estimation of the (S_{pmk}) is also studied. The asymptotic confidence interval is also constructed for (S_{pmk}). The simulation results indicate that estimators for both the unknown parameters of the new distribution and the (S_{pmk}) provide reasonable results. Some practical analyses are also performed on both the new distribution and the (S_{pmk}). The results of the conducted data analysis indicate that the new distribution yields effective outcomes in modeling lifetime data in the literature. Similarly, the data analyses performed for (S_{pmk}) illustrate that the new distribution can be utilized for process capability indices by quality controllers.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"821 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective optimization of virtual machine migration among cloud data centers 云数据中心间虚拟机迁移的多目标优化

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09950-2

Francisco Javier Maldonado Carrascosa, Doraid Seddiki, Antonio Jiménez Sánchez, Sebastián García Galán, Manuel Valverde Ibáñez, Adam Marchewka

{"title":"Multi-objective optimization of virtual machine migration among cloud data centers","authors":"Francisco Javier Maldonado Carrascosa, Doraid Seddiki, Antonio Jiménez Sánchez, Sebastián García Galán, Manuel Valverde Ibáñez, Adam Marchewka","doi":"10.1007/s00500-024-09950-2","DOIUrl":"https://doi.org/10.1007/s00500-024-09950-2","url":null,"abstract":"Workload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"43 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eno classification and regression neural networks for numerical approximation of discontinuous flow problems 用于非连续流问题数值逼近的埃诺分类和回归神经网络

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09944-0

Vikas Kumar Jayswal, Prashant Kumar Pandey, Ritesh Kumar Dubey

{"title":"Eno classification and regression neural networks for numerical approximation of discontinuous flow problems","authors":"Vikas Kumar Jayswal, Prashant Kumar Pandey, Ritesh Kumar Dubey","doi":"10.1007/s00500-024-09944-0","DOIUrl":"https://doi.org/10.1007/s00500-024-09944-0","url":null,"abstract":"Learning high order non-oscillatory polynomial approximation procedures which form the backbone of high order numerical solution of partial differential equations is challenging. The major issue is to pose these procedures as a learning problem and generate suitable synthetic data set which suffice learning it with small neural networks. In this work, we pose an arc-length based essentially non-oscillatory (ENOL) reconstruction algorithm as machine learning problem. A novel way to construct the synthetic data using ENOL algorithm along with basic smooth and piece-wise continuous functions is given. Small vanilla regression and classification neural networks are trained to learn third order (ENOL) polynomial approximation procedure. The metric of trained ENO classification and regression networks is presented and commented. These trained models are implemented in numerical solver to compute the solution of test problems of hyperbolic conservation laws. The presented numerical results show that ENO classification network gives results comparable to the exact ENOL reconstruction whereas ENO regression network performs poorly both in terms of convergence and resolving the discontinuities.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"43 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explicit and implicit numerical investigations of one-dimensional heat equation based on spline collocation and Thomas algorithm 基于样条配位和托马斯算法的一维热方程显式和隐式数值研究

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09925-3

Saumya Ranjan Jena, Archana Senapati

引用次数: 0

Interval-based multi-objective metaheuristic honey badger algorithm 基于区间的多目标元启发式蜜獾算法

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09893-8

Peixin Huang, Guo Zhou, Yongquan Zhou, Qifang Luo

{"title":"Interval-based multi-objective metaheuristic honey badger algorithm","authors":"Peixin Huang, Guo Zhou, Yongquan Zhou, Qifang Luo","doi":"10.1007/s00500-024-09893-8","DOIUrl":"https://doi.org/10.1007/s00500-024-09893-8","url":null,"abstract":"Optimization problem involving interval parameters and multiple conflicting objectives are called multi-objective optimization problems with interval parameters (IMOPs), which are common and hard to be solved effectively in practical applications. An interval multi-objective honey badger algorithm (IMOHBA) is proposed to address the IMOPs in this paper. Firstly, the (mu) metric is employed to assess the Pareto dominance relationship among interval individuals, which reflects the quality of the optimal solutions. Secondly, the crowding distance suitable for the interval objective is utilized to reflect the distribution of the optimal solution. Finally, the candidate solutions are ranked and selected by the non-dominated sorting method. To validate the performance of IMOHBA, it is tested on 19 benchmark IMOPs as well as an interval multi-objective scheduling problem for underwater wireless sensor networks and compared with three state-of-the-art algorithms. The experimental results demonstrate the superiority and strong competitiveness of IMOHBA in addressing IMOPs, exhibiting improved convergence and broader exploration capabilities of the solution space. These findings further validate the effectiveness and feasibility of IMOHBA, highlighting its unique advantage in solving IMOPs.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved particle swarm optimization algorithm with distributed time-delays of evolved acceleration coefficients and adaptive weights 一种改进的粒子群优化算法，带有进化加速系数的分布式时间延迟和自适应权重

IF 4.1 3区计算机科学

Soft Computing Pub Date : 2024-07-24 DOI: 10.1007/s00500-024-09813-w

Xin Tian, Jianhua Hu, Yan Song, Guoliang Wei

{"title":"An improved particle swarm optimization algorithm with distributed time-delays of evolved acceleration coefficients and adaptive weights","authors":"Xin Tian, Jianhua Hu, Yan Song, Guoliang Wei","doi":"10.1007/s00500-024-09813-w","DOIUrl":"https://doi.org/10.1007/s00500-024-09813-w","url":null,"abstract":"Particle swarm optimization (PSO) is a classical computational method that optimizes a problem by iteratively trying to find the optimal solution. It still suffers somes defects such as poor local search ability, low search accuracy and premature convergence, especially in high-dimensional complex problems. In order to address these issues, this paper has proposed a novel PSO algorithm with distributed delays of adaptive weights and evolved acceleration coefficients (PSO-DWC). The main idea of the proposed improved PSO algorithm is three-fold: (1) a mechanism is introduced to evaluate the current evolutionary state by evolutionary factors of the swarm and to predict the next state by a probability transition matrix; (2) distributed time-varying time-delays are added into the velocity updated model; (3) adaptive inertia weight varies according to evolutionary factors, which describes the population distribution information; and newly-introduced evolved acceleration coefficients are determined by the predict next evolutionary state of the swarm. Owing to the promising issues mentioned above, the PSO-DWC algorithm has the advantages of keeping the diversity of particles, balancing the local and global search abilities and reaching to an acceptable solution. Experiments on twenty well-known benchmark functions have demonstrated that the proposed PSO-DWC algorithm has a superior performance over other five well-known PSO algorithms in high dimensional search space. Statistical significance tests verify the superiority of the new algorithm. Therefore it can be concluded that the novel PSO-DWC algorithm is able to solve the optimization problems with powerful global search and efficient convergence.","PeriodicalId":22039,"journal":{"name":"Soft Computing","volume":"42 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141779087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0