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2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)最新文献

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Active learning over evolving data streams using paired ensemble framework 使用配对集成框架对不断变化的数据流进行主动学习
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449823
Wenhua Xu, Fengfei Zhao, Zhengcai Lu
Stream data is considered as one of the main sources of big data. The inherent scarcity of labeled instances and the underlying concept drift have posed significant challenges on stream data classification in practice. A paired ensemble active learning framework is proposed to tackle the challenges. First, an ensemble model consists of two base classifiers is exploited to detect the changes over time, as well as to make prediction on new instances. Second, two active learning strategies work alternatively to find out the most informative instances without missing the potential changes happened anywhere in the instance space. Third, the informativeness of an instance is measured by a margin based metric, and it can effectively capture uncertain instances. Experimental results on real-world datasets demonstrate that the proposed approach can achieve good predictive accuracy on data streams.
流数据被认为是大数据的主要来源之一。标记实例固有的稀缺性和潜在的概念漂移给流数据分类带来了巨大的挑战。提出了一种配对集成主动学习框架来解决这些挑战。首先,利用由两个基本分类器组成的集成模型来检测随时间的变化,并对新实例进行预测。其次,两种主动学习策略交替工作,以找出最有信息的实例,而不会错过实例空间中任何地方发生的潜在变化。第三,采用基于余量的度量来度量实例的信息量,能够有效地捕获不确定的实例。在实际数据集上的实验结果表明,该方法对数据流具有较好的预测精度。
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引用次数: 15
A new time-dependent algorithm for post enrolment-based course timetabling problem 基于入学后课程排课问题的一种新的时变算法
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449863
Hongteng Wu, Adi Lin, Defu Zhang, Carine Pierrette Mukamakuza
In this paper, we propose a new time-dependent heu-ristic algorithm for post enrolment-based course timetabling prob-lem. The algorithm operates in two stages: a constructive phase is proposed to insert events into the timetable whilst obeying most hard constraints, and a hill-climbing phase is designed to ensure the timetable meeting all the hard constraints. Each stage is allocated a time limit. The experimental results on instances from the second international timetabling competition show that our algorithm is efficient and competitive with other effective algorithms.
在本文中,我们提出了一种新的基于时间的基于入学后的课程排课问题的时间依赖算法。该算法分为两个阶段:构造阶段在满足大多数硬约束的情况下将事件插入时间表;爬坡阶段在满足所有硬约束的情况下设计时间表。每个阶段都有一个时间限制。在第二届国际排课竞赛实例上的实验结果表明,该算法具有较高的效率和较强的竞争力。
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引用次数: 1
Co-clustering of diseases, genes, and drugs for identification of their related gene modules 疾病、基因和药物的共聚类,用于鉴定其相关基因模块
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449860
A. Koohi, H. Homayoun, Jie Xu, M. Orooji
Finding gene clusters that can be shared between drugs and diseases plays an important role in drug discovery. Targeting disease causing genes directly in drug development can increase the chance of drug approval through the clinical phase. This paper introduces a new co-clustering approach on the tripartite graph of genes, drugs, and diseases. As a result of co-clustering, gene modules and their related drugs and diseases are identified. It is shown that identified gene modules are functionally related. In addition the resulted gene modules are closely connected to each other in the protein-protein interaction network compared to that of random gene selection. The resulting gene modules can be used for investigating the genes that can be targeted with new drugs for treatment of diseases that are co-clustered with them. The proposed method is scalable and can be used for other multi-view graph co-clustering applications like social networks.
寻找药物和疾病之间可以共享的基因簇在药物发现中起着重要作用。在药物开发中直接针对致病基因可以增加药物通过临床阶段批准的机会。本文介绍了一种新的基因、药物和疾病三方图的共聚类方法。作为共聚类的结果,基因模块及其相关的药物和疾病被识别。结果表明所鉴定的基因模块在功能上是相关的。此外,与随机基因选择相比,所得到的基因模块在蛋白质-蛋白质相互作用网络中彼此紧密相连。由此产生的基因模块可用于研究可用于治疗与它们共同聚集的疾病的新药靶向的基因。该方法具有可扩展性,可用于其他多视图图共聚类应用,如社交网络。
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引用次数: 1
Computational intelligent color normalization for wheat plant images to support precision farming 小麦植物图像的计算智能颜色归一化以支持精准农业
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449816
S. B. Sulistyo, W. L. Woo, S. Dlay
Image colors are considerably affected by the intensity of the light source. In this paper, we propose a color constancy method using neural networks fusion to normalize images captured under sunlight with a variation of light intensities. A genetic algorithm is also applied to optimize the color normalization. A 24-patch Macbeth color checker is utilized as the reference to normalize the images. The results of our proposed method is superior to other methods, i.e. the conventional gray world and scale-by-max methods, as well as linear model and single neural network method. Furthermore, the proposed method can be used to normalize wheat plant images captured under various light intensities.
图像颜色受光源强度的影响很大。在本文中,我们提出了一种使用神经网络融合的颜色恒常性方法,对光照强度变化的阳光下捕获的图像进行归一化。采用遗传算法对颜色归一化进行优化。使用24块麦克白颜色检查器作为参考,对图像进行规范化。该方法的结果优于传统的灰色世界和最大比例法,也优于线性模型和单神经网络方法。此外,该方法可用于在不同光强下捕获的小麦植物图像的归一化。
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引用次数: 2
Short term traffic flow prediction based on on-line sequential extreme learning machine 基于在线顺序极值学习机的短期交通流预测
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449818
Zhiyuan Ma, Guangchun Luo, Dijiang Huang
Traffic flow cannot be predicted solely based on historical data due to its high dynamics and sensitivity to emergency situations. In this paper, a real traffic data collected from 2011 to 2014 is used, and an adaptive prediction model based on a variant of Extreme Learning Machine (ELM), namely On-line Sequential ELM with forgetting mechanism, is built. The model has the capability of updating itself using incoming data, and adapts to the changes in real time. However, limitations, such as the requirements of large number of neurons and dataset size for initialization, are discovered in practice. To improve the applicability, another scheme involving sequential updating and network reconstruction is proposed. The experimental results show that, compared with the previous method, the proposed one has better performance in time while achieving the similar accuracy.
由于交通流量的高度动态性和对紧急情况的敏感性,不能仅根据历史数据预测交通流量。本文以2011 - 2014年的真实交通数据为研究对象,建立了一种基于极限学习机(ELM)变体的自适应预测模型,即带遗忘机制的在线顺序ELM。该模型具有利用输入数据进行自我更新的能力,并能实时适应数据的变化。然而,在实践中发现了一些局限性,例如初始化需要大量的神经元和数据集大小。为了提高适用性,提出了另一种涉及顺序更新和网络重构的方案。实验结果表明,与之前的方法相比,本文提出的方法在获得相似精度的同时,在时间上具有更好的性能。
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引用次数: 36
Subspace video stabilization based on matrix transformation and Bezier curve 基于矩阵变换和Bezier曲线的子空间视频稳像
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449837
Zheng Zhao, Xiaohong Ma
Video stabilization improves video quality by removing undesirable jitter to receive stable and comfortable video sequences. This paper proposes a new approach for subspace video stabilization. To make feature trajectories factorization more accurate, we segment feature trajectories into fragments to construct local trajectory matrices, then obtain smooth trajectories based on subspace constraint, matrix transformation and Bezier curve. Finally, according to the original feature points and the final corresponding stable feature points, we use mesh warp to receive high-quality and plausible videos. Experiments show that our method can generate comparable results with regard to some other state-of-the-art video stabilization methods, furthermore in some scenes our results are better than theirs.
视频稳定通过消除不受欢迎的抖动来提高视频质量,以接收稳定和舒适的视频序列。本文提出了一种新的子空间视频稳像方法。为了提高特征轨迹分解的精度,我们将特征轨迹分割成碎片构造局部轨迹矩阵,然后基于子空间约束、矩阵变换和Bezier曲线得到光滑轨迹。最后,根据原始特征点和最终对应的稳定特征点,利用网格经纱获得高质量、可信的视频。实验表明,我们的方法可以产生与其他一些最先进的视频稳定方法相当的结果,并且在某些场景中我们的结果比他们的更好。
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引用次数: 0
A novel approach based on differential evolution for blind deconvolution 一种基于差分进化的盲反卷积新方法
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449813
Kai Kang, Yang Cao, Zengfu Wang
Blind deconvolution refers to a class of problems of recovering a sharp version of a blurred image without any information about the blur kernel. In this paper, we propose a novel approach for blind deconvolution based on differential evolution (DE) algorithm, which is arguably one of the most powerful stochastic real-parameter optimization algorithms. Thanks to DE algorithm, various non-conjugate kernel priors, which can be used to effectively restrain the estimated kernel from unexpected situations such as delta kernel, are prone to be introduced to the proposed approach. In order to accelerate the computation speed, we relax the image prior, utilizing the Gaussian prior instead of the well-known sparse prior. Then the optimization problem turns to be convex, what's more, the optimal solution can be effectively solved in frequency domain. In addition, we use the kernel prior cost to propose candidate solutions to speed up the computation further. Finally, given the estimated kernel, we estimate the sharp image by sparse prior. Experimental results and comparisons demonstrate the effectiveness of our method.
盲反卷积是指在没有任何关于模糊核的信息的情况下恢复模糊图像的清晰版本的一类问题。本文提出了一种基于差分进化算法的盲反卷积算法,差分进化算法是目前最强大的随机实参数优化算法之一。由于DE算法,可以将各种非共轭核先验引入到该方法中,这些非共轭核先验可以有效地抑制估计核不受诸如δ核等意外情况的影响。为了加快计算速度,我们放松了图像先验,利用高斯先验代替了众所周知的稀疏先验。这样,优化问题就变成了一个凸问题,并且可以在频域有效地求解最优解。此外,我们利用核先验代价提出候选解,进一步加快计算速度。最后,给出估计的核,利用稀疏先验估计出清晰图像。实验结果和对比验证了该方法的有效性。
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引用次数: 2
Improvement of spatial data clustering algorithm in city location 城市定位空间数据聚类算法的改进
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449812
Qibing Zhu
Spatial data mining is a new research direction in the field of Data Mining. In recent years, with the continuous development of data mining technology, spatial data attracts more and more attentions of scholars and experts. Spatial clustering analysis is an important part of spatial data mining. Nowadays, spatial clustering analysis has become more and more mature, widely used in various fields. Spatial clustering analysis algorithm can deeply discover the knowledge which hidden in the geospatial information, find out the representative node of one or a number of spatial data collection, discovery the law of the spatial distribution. Classic clustering algorithm basing on partition widely used in the field of cities planning and provide valuable reference. This paper is based on the spatial data mining method, analysis and optimize the spatial data clustering algorithm in the Location Problem in the city, providing scientific location decisions.
空间数据挖掘是数据挖掘领域的一个新的研究方向。近年来,随着数据挖掘技术的不断发展,空间数据越来越受到学者和专家的关注。空间聚类分析是空间数据挖掘的重要组成部分。如今,空间聚类分析已经越来越成熟,广泛应用于各个领域。空间聚类分析算法可以深入发现隐藏在地理空间信息中的知识,找出一个或多个空间数据集合的代表性节点,发现空间分布的规律。经典的基于分区的聚类算法在城市规划领域得到了广泛的应用并提供了有价值的参考。本文基于空间数据挖掘方法,对城市选址问题中的空间数据聚类算法进行分析和优化,为科学的选址决策提供依据。
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引用次数: 2
Adaptive CDN-based bandwidth conserving algorithm for Mobile IPTV 基于cdn的移动IPTV自适应带宽节约算法
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449859
B. Abubakar, M. Petridis, D. Gill, Saeed Malekshahi Gheytassi
Network bandwidth and server capacity are gradually becoming overloaded due to high demand and rapid evolution of high quality multimedia services over the Internet. Internet Protocol Television (IPTV) is among the multimedia services that demand more of network and server resources, especially with the emergence of Mobile IPTV. It is imperative for service providers to maintain good quality management services in order to satisfy their clients. To improve the required Quality of Service (QoS) and Quality of Experience (QoE), a Content Distribution Network (CDN) approach is being adopted and used by service providers, where contents are replicated over multiple distributed servers with the best server selected to serve an incoming request. In this paper, we propose an Adaptive CDN-Based Bandwidth Conserving Algorithm for Mobile IPTV that adapts to different server bandwidth capacity in order to improve the QoS, which in turn will provide the required QoE. Results from the simulation tests show that the proposed algorithm performed well in adapting to different server bandwidth level to switch between using the server or client to serve an incoming service requests. It also confirmed that the proposed algorithm outperformed the normal CDN-based IPTV system in server load reduction, high throughput and low end-to-end delay.
由于互联网上高质量多媒体业务的高需求和快速发展,网络带宽和服务器容量逐渐过载。互联网协议电视(IPTV)是对网络和服务器资源要求较高的多媒体业务之一,尤其是随着移动IPTV的出现。服务提供者必须保持良好的质量管理服务,以满足他们的客户。为了改善所需的服务质量(QoS)和体验质量(QoE),服务提供商正在采用和使用内容分发网络(CDN)方法,其中内容在多个分布式服务器上复制,并选择最佳服务器来服务传入请求。本文提出了一种基于自适应cdn的移动IPTV带宽保护算法,该算法可以适应不同的服务器带宽容量,从而提高QoS,从而提供所需的QoE。仿真测试结果表明,该算法能够很好地适应不同的服务器带宽水平,在使用服务器或客户端之间切换以处理传入的服务请求。该算法在降低服务器负载、高吞吐量和低端到端延迟等方面优于普通的基于cdn的IPTV系统。
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引用次数: 2
A new improved fruit fly optimization algorithm for traveling salesman problem 旅行商问题的一种改进果蝇优化算法
Pub Date : 2016-04-11 DOI: 10.1109/ICACI.2016.7449797
Lvjiang Yin, Xinyu Li, Liang Gao, Chao Lu
Traveling salesman problem (TSP) which is a classic combinational optimization problem has a wide range of applications in many areas. Many researchers focus on this problem and propose several algorithms. However, it was proved to be NP-hard, which is very difficult to be solved. No algorithm can solve any types of this problem effectively. In order to propose an effective algorithm for TSP, this paper improves the fruit fly optimization algorithm (FOA) proposed recently. As far as we know, the FOA has not yet been applied to solve TSP. Therefore, several modifications of FOA have to be made to meet the characteristics of TSP. Based on the whole search framework and the essence of FOA, some operations of particle swarm optimization (PSO) have been introduced into this method. In the smell search phase, the cluster mechanism of the fruit flies has been used to copy flies to one point and the mutation operation of genetic algorithm is used as the method of information exchanging among fruit flies for random search. In the visual search phase, the generalized PSO is applied to balance the global search and local search abilities of proposed algorithm. To evaluate the performance of proposed algorithm, some experiments and comparisons with other reported algorithms have been conducted. The results show the feasibility and effectiveness of proposed algorithm in solving TSP.
旅行商问题(TSP)是一个经典的组合优化问题,在许多领域有着广泛的应用。许多研究者关注这个问题,并提出了几种算法。然而,它被证明是np困难的,这是非常难以解决的。没有任何一种算法可以有效地解决这类问题。为了提出一种有效的TSP算法,本文对最近提出的果蝇优化算法(FOA)进行了改进。据我们所知,FOA还没有应用于解决TSP。因此,必须对FOA进行若干修改,以满足TSP的特点。在整个搜索框架的基础上,结合FOA的本质,将粒子群优化(PSO)的一些操作引入该方法。在气味搜索阶段,利用果蝇的聚类机制将果蝇复制到一点,利用遗传算法的突变操作作为果蝇间信息交换的方法进行随机搜索。在视觉搜索阶段,应用广义粒子群算法平衡算法的全局搜索能力和局部搜索能力。为了评估该算法的性能,我们进行了一些实验,并与其他已报道的算法进行了比较。实验结果表明了该算法求解TSP问题的可行性和有效性。
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引用次数: 9
期刊
2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)
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