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1.
Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Different data classification models were developed to foresee the financial crisis of an organization using their historical data. One important step towards the development of accurate financial crisis prediction (FCP) model involves the selection of appropriate variables (features) which are relevant for the problems at hand. This is termed as feature selection problem which helps to improve the classification performance. This paper proposes an Ant Colony Optimization (ACO) based financial crisis prediction (FCP) model which incorporates two phases: ACO based feature selection (ACO-FS) algorithm and ACO based data classification (ACO-DC) algorithm. The proposed ACO-FCP model is validated using a set of five benchmark dataset includes both qualitative and quantitative. For feature selection design, the developed ACO-FS method is compared with three existing feature selection algorithms namely genetic algorithm (GA), Particle Swarm Optimization (PSO) algorithm and Grey Wolf Optimization (GWO) algorithm. In addition, a comparison of classification results is also made between ACO-DC and state of art methods. Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts. In consequence, this study strongly recommends that the proposed ACO-FCP model is highly competitive than traditional and other artificial intelligence techniques. 相似文献
2.
隐私数据挖掘是数据挖掘的一个重要研究方向,它旨在研究在数据挖掘过程中如何保护私有的和敏感的数据不被泄露。文章阐述几种常用的隐私数据挖掘算法,分析它们的技术特点,文末对几种隐私数据挖掘技术进行总结与展望。 相似文献
3.
《Information processing & management》2022,59(5):103005
In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning. 相似文献
4.
This article presents a novel tuning design of Proportional-Integral-Derivative (PID) controller in the Automatic Voltage Regulator (AVR) system by using Cuckoo Search (CS) algorithm with a new time domain performance criterion. This performance criterion was chosen to minimize the maximum overshoot, rise time, settling time and steady state error of the terminal voltage. In order to compare CS with other evolutionary algorithms, the proposed objective function was used in Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for PID design of the AVR system. The performance of the proposed CS based PID controller was compared to the PID controllers tuned by the different evolutionary algorithms using various objective functions proposed in the literature. Dynamic response and a frequency response of the proposed CS based PID controller were examined in detail. Moreover, the disturbance rejection and robustness performance of the tuned controller against parametric uncertainties were obtained, separately. Energy consumptions of the proposed PID controller and the PID controllers tuned by the PSO and ABC algorithms were analyzed thoroughly. Extensive simulation results demonstrate that the CS based PID controller has better control performance in comparison with other PID controllers tuned by the PSO and ABC algorithms. Furthermore, the proposed objective function remarkably improves the PID tuning optimization technique. 相似文献
5.
[目的/意义]为了合理化决策,通常一个政府部门会根据业务需求向其他部门共享某类数据,为本部门管理或服务决策提供辅助参考依据。数据共享在其中至关重要,但若在没有适当预防措施的情况下就共享政务数据,将容易造成隐私信息的泄露。[方法/过程]针对政府部门间共享统计数据的场景,提出一种基于本地化差分隐私的政务数据共享方法。该方法在算法Generalized randomized response(GRR)的基础上引入数据分箱思想,通过等宽分箱将数据记录分入更小的数据域范围内,以克服当前隐私保护算法在数据域较大且数据量较少时统计误差大的问题。[结果/结论]将所提算法与GRR算法在仿真数据集和真实数据集上均进行了对比分析,实验结果表明该算法可有效降低统计误差,并能在不同分布和数据域大小下保持其效用性。 相似文献
6.
《Information processing & management》2016,52(3):387-398
Many problems in data mining involve datasets with multiple views where the feature space consists of multiple feature groups. Previous studies employed view weighting method to find a shared cluster structure underneath different views. However, most of these studies applied gradient optimization method to optimize the cluster centroids and feature weights iteratively and made the final partition local optimal. In this work, we proposed a novel bi-level weighted multi-view clustering method with emphasizing fuzzy weighting on both view and feature. Furthermore, an efficient global search strategy that combines particle swarm optimization and gradient optimization was proposed to solve the induced non-convex loss function. In the experimental analysis, the performance of the proposed method was compared with five state-of-the-art weighted clustering algorithms on three real-world high-dimensional multi-view datasets. 相似文献
7.
通过对相关文献的回顾,本文对国际网络隐私研究进行了计量分析,发现现有的研究主题集中在"基于移动位置服务的隐私问题研究"、"隐私保护算法研究"、"隐私保护技术研究"、"云计算的隐私安全研究"、"网络隐私权限控制研究"、"社交网站和电子商务中的用户隐私态度与行为研究"、"青少年敏感信息的隐私与安全保护研究"、"数据公开共享策略与国家治理研究"8个方面。研究发现当前研究中存在"利用数据纵向分析网络隐私主题随时间发展变化的研究较少"、"对于一些发展中国家的隐私问题关注度较低"、"国家的治理与业界的隐私政策相对独立缺乏联系"等问题,"大数据时代移动信息和社交信息挖掘过程中的隐私问题"、"在线医疗的隐私问题"等是未来研究值得关注的领域。 相似文献
8.
《Information processing & management》2023,60(1):103109
With an increase in the number of data instances, data processing operations (e.g. clustering) requires an increasing amount of computational resources, and it is often the case that for considerably large datasets such operations cannot be executed on a single workstation. This requires the use of a server computer for carrying out the operations. However, to ensure privacy of the shared data, a privacy preserving data processing workflow involves applying an encoding transformation on the set of data points prior to applying the computation. This encoding should ideally cater to two objectives—first, it should be difficult to reconstruct the data, second, the results of the operation executed on the encoded space should be as close as possible to the results of the same operation executed on the original data. While standard encoding mechanisms, such as locality sensitive hashing, caters to the first objective, the second objective may not always be adequately satisfied.In this paper, we specifically focus on ‘clustering’ as the data processing operation. We apply a deep metric learning approach to learn a parameterized encoding transformation function with an objective to maximize the alignment of the clusters in the encoded space to those in the original data. We conduct experimentation on four standard benchmark datasets, particularly MNIST, Fashion-MNIST (each dataset contains 70K grayscale images), CIFAR-10 consisting of 60K color images and 20-Newsgroups containing 18K news articles. Our experiments demonstrate that the proposed method yields better clusters in comparison to approaches where the encoding process is agnostic of the clustering objective. 相似文献
9.
We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search. 相似文献
10.
In recent years, the deployment of Cloud Computing (CC) has become more popular both in research and industry applications, arising form various fields including e-health, manufacturing, logistics and social networking. This is due to the easiness of service deployment and data management, and the unlimited provision of virtual resources (VR). In simple scenarios, users/applications send computational or storage tasks to be executed in the cloud, by manually assigning those tasks to the available computational resources. In complex scenarios, such as a smart city applications, where there is a large number of tasks, VRs, or both, task scheduling is exposed as an NP-Hard problem. Consequently, it is preferred and more efficient in terms of time and effort, to use a task scheduling automation technique. As there are many automated scheduling solutions proposed, new possibilities arise with the advent of Fog Computing (FC) and Blockchain (BC) technologies. Accordingly, such automation techniques may help the quick, secure and efficient assignment of tasks to the available VRs. In this paper, we propose an Ant Colony Optimization (ACO) algorithm in a Fog-enabled Blockchain-assisted scheduling model, namely PF-BTS. The protocol and algorithms of PF-BTS exploit BC miners for generating efficient assignment of tasks to be performed in the cloud’s VRs using ACO, and award miner nodes for their contribution in generating the best schedule. In our proposal, PF-BTS further allows the fog to process, manage, and perform the tasks to enhance latency measures. While this processing and managing is taking place, the fog is enforced to respect the privacy of system components, and assure that data, location, identity, and usage information are not exposed. We evaluate and compare PF-BTS performance, with a recently proposed Blockchain-based task scheduling protocol, in a simulated environment. Our evaluation and experiments show high privacy awareness of PF-BTS, along with noticeable enhancement in execution time and network load. 相似文献
11.
分布式水循环模型的参数优化算法比较及应用 总被引：1，自引：0，他引：1
分布式水文模型的优势在于还原水文过程的时空变异性,可以很好地模拟和反映各种水文要素和下垫面因素的时空分布不均匀性。由此也导致模型参数过多,在子流域过多的情况下,人工调节参数繁琐复杂,应用优化算法实现参数自动调节成为首选。本文选取石羊河流域九条岭站1988-2005年实测径流资料,分别应用SCE-UA算法、遗传算法(GA)和粒子群算法(PSO)对分布式水循环模型(时变增益模型)进行参数率定,对比3种算法的收敛速度、所需迭代次数和算法稳定性。结果表明:通过SCE-UA、GA和PSO的优化,模型水平衡系数都控制在0.0左右,而相关系数和效率系数分别能达到0.90和0.84以上,模拟精度较好。但粒子群算法的全局搜索能力和收敛速度优于SCE-UA和遗传算法,所需迭代次数最少,初值敏感性小,更适合时变增益模型的参数寻优,有很高的扩展性和改进潜力。 相似文献
12.
在FDM算法的基础上,提出了一种改进的并行关联规则挖掘算法FDM_DT,此算法利用DHP算法中的Hash表技术改进了2阶侯选项集的生成过程,并采用Apriori Tid算法中的Tid表技术对事务数据库中的事务数进行有效消减。因此,此算法在处理大规模数据时有较高的综合效率。 相似文献
13.
［目的］揭示人工智能（AI）迭代发展中大数据挖掘的风险、成因与对策,为打造智能化协同创新生态链提供重要支撑。［方法］分类探讨AI图景下大数据挖掘中智能采集侵害数据产权、关联勾勒突破数据匿名、黑盒疏漏打破动态平等,以及算法决策冲击自由抉择等风险及其成因,提出数据挖掘的原则与策略。［结果/结论］参与者之间参差不齐的管控意识与处理能力、复杂多元的利益诉求以及缺少政策法规集中调控等导致上述风险,亟待严格遵循平等参与、优质输入、彻底拭除以及公允运转等挖掘原则,主动采取充实数据产权规定、丰富数据隐私条款、明确挖掘主体资质与公权参与标准、推动签署数据跨境统一处置方案以及健全相关救济措施等具体对策。 相似文献
14.
Web日志中包含了大量的用户浏览信息,如何有效地从中挖掘出用户浏览模式就尤为重要了。本文在分析现有用户浏览模式挖掘算法存在问题的基础上,根据Web日志的特点,对关联规则挖掘算法进行改进,提出了基于滑动窗口的浏览模式挖掘算法TBPM。并在此算法基础上设计了增量更新算法,对实际数据的实验结果验证了本算法的有效性。 相似文献
15.
Amir-R. Khorsand 《Journal of The Franklin Institute》2007,344(5):595-612
Evolutionary structural design has been the topic of much recent research; however, such designs are usually hampered by the time-consuming stage of prototype evaluations using standard finite element analysis (FEA). Replacing the time-consuming FEA by neural network approximations may be a computationally efficient alternative, but the error in such approximation may misguide the optimization procedure. In this paper, a multi-objective meta-level (MOML) soft computing-based evolutionary scheme is proposed that aims to strike a balance between accuracy vs. computational efficiency and exploration vs. exploitation. The neural network (NN) is used here as a pre-filter when fitness is estimated to be of lesser significance while the standard FEA is used for solutions that may be optimal in their current population. Furthermore, a fuzzy controller updates parameters of the genetic algorithm (GA) in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of the membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane wing. General applicability of the resulting optimization algorithm is then tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fuzzy-GA-NN, a GA-NN, as well as a simple GA that only uses only FEA, in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms. Specifically, the proposed approach finds better structural designs more consistently while being computationally more efficient. 相似文献
16.
This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of ?50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms. 相似文献
17.
基于现行数据隐私问题日益严重,如何防止数据挖掘过程中隐私信息的泄漏,将是一个重要的研究议题。本文主要针对关联规则挖掘技术,从安全多方计算方面探讨隐私信息的保护,提出适用于挖掘水平分割数据的保护机制。方法设计采用集中式挖掘,过程中加入信息安全技术以确保参与单位的数据隐私,以求在隐私保护和知识获取间取得一个平衡。 相似文献
18.
In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters. 相似文献
19.
《Journal of The Franklin Institute》2023,360(9):6194-6210
Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. These data are often collected and stored across different institutions (banks, hospitals, etc.), or termed cross-silo. In this context, cross-silo federated learning has become prominent to tackle the privacy issues, where only model updates will be transmitted from institutions to servers without revealing institutions’ private information. In this paper, we propose a cross-silo federated XGBoost approach to solve the federated anomaly detection problem, which aims to identify abnormalities from extremely unbalanced datasets (e.g., credit card fraud detection) and can be considered a special classification problem. We design two privacy-preserving mechanisms that are tailored to the federated XGBoost: anonymity based data aggregation and local differential privacy. In the anonymity based data aggregation scenario, we cluster data into different groups and using a cluster-level data feature to train the model. In the local differential privacy scenario, we design a federated XGBoost framework by incorporate differential privacy in parameter transmission. Our experimental results over two datasets show the effectiveness of our proposed schemes compared with existing methods. 相似文献
20.
This paper reports the results of a study of MySpace use, by Sydney high school children. It examines the reasons why children disclose information on the website and their understanding of some of the privacy issues involved. The study provides a useful beginning for further work in this area. Regression is used to validate a proposed model of the factors influencing information disclosure and cluster analysis provides an indication of characteristics shared by children who disclose sensitive information, such as name, address and telephone number. It also suggests that children who are taught to value privacy are less likely to disclose sensitive information on-line. 相似文献