Conceptual procedure for ranking fuzzy numbers based on adaptive two-dimensions dominance

Many methods for ranking of fuzzy numbers have been proposed. However, these methods just can apply to rank some types of fuzzy numbers (i.e. normal, non-normal, positive, and negative fuzzy numbers), and many ranking cases can just rank by their graphs intuitively. So, it is important to use proper methods in the right condition. In this paper, a conceptual procedure is proposed to describe how to use intuitive ranking and some technical ranking methods properly. We also introduce a new ranking fuzzy numbers approach that can adjust experts confidence and optimistic index of decision maker using two parameters ( and ) to handle the problems and find the best solutions. After illustrate many numerical examples following our conceptual procedure the ranking results are validity.     

A systematic procedure to obtain a preferable and robust ranking of units

This paper introduces one systematic procedure for the manager of an organization to assess units under its governance using multiple performance indices. The goal of this systematic procedure is to assist the manager in obtaining a preferable and robust ranking result for units. In this procedure, for all units, one common set of weights attached to the performance indices is determined in order to maximize the group's comprehensive score. Then, using the common set of weights, each unit's comprehensive score is evaluated and compared for ranking. In order to obtain the preferable ranking, the manager's subjective preference is considered and formulated by the virtual weights restrictions while determining the common weights in the procedure. The procedure is applied in order to obtain a robust ranking by modifying the boundary of the feasible region of virtual weights restrictions in each assessment. The final statistical ranking of all assessments provides the manager with one robust ranking, which is invariant in different feasible regions of virtual weights restrictions in the numerical example.     

A parameterless feature ranking algorithm based on MI

A parameterless feature ranking approach is presented for feature selection in the pattern classification task. Compared with Battiti's mutual information feature selection (MIFS) and Kwak and Choi's MIFS-U methods, the proposed method derives an estimation of the conditional MI between the candidate feature f_i and the output class C given the subset of selected features S, i.e. I(C;f_iS), without any parameters like β in MIFS and MIFS-U methods to be preset. Thus, the intractable problem can be avoided completely, which is how to choose an appropriate value for β to achieve the tradeoff between the relevance to the output classes and the redundancy with the already-selected features. Furthermore, a modified greedy feature selection algorithm called the second order MI feature selection approach (SOMIFS) is proposed. Experimental results demonstrate the superiority of SOMIFS in terms of both synthetic and benchmark data sets.     

Curve-based monotonicity: a generalization of directional monotonicity

In this work we propose a generalization of the notion of directional monotonicity. Instead of considering increasingness or decreasingness along rays, we allow more general paths defined by curves in the n-dimensional space. These considerations lead us to the notion of α-monotonicity, where α is the corresponding curve. We study several theoretical properties of α-monotonicity and relate it to other notions of monotonicity, such as weak monotonicity and directional monotonicity.     

A ranking of software engineering measures based on expert opinion

This research proposes a framework based on expert opinion elicitation, developed to select the software engineering measures which are the best software reliability indicators. The current research is based on the top 30 measures identified in an earlier study conducted by Lawrence Livermore National Laboratory. A set of ranking criteria and their levels were identified. The score of each measure for each ranking criterion was elicited through expert opinion and then aggregated into a single score using multiattribute utility theory. The basic aggregation scheme selected was a linear additive scheme. A comprehensive sensitivity analysis was carried out. The sensitivity analysis included: variation of the ranking criteria levels, variation of the weights, variation of the aggregation schemes. The top-ranked measures were identified. Use of these measures in each software development phase can lead to a more reliable quantitative prediction of software reliability.     

图层约束下的点状自然灾害风险地图综合

点状自然灾害现象如地震、滑坡等,由于其特殊性,灾害风险与其周边的地理环境有着复杂的联系和相互作用,孕灾环境对灾情具有放大或缩小的效应,在制图综合过程中,不能只考虑单个灾害点个体,而因将与之相关的各因素综合考虑,从而判定其风险范围。基于此,在地理学与地图学的基础上,从灾害系统的角度考虑,探讨基于图层约束(LC)和模糊推理系统(FIS)相结合的点状现象自动综合的适用性问题,重点阐述基于图层约束理论和FIS算法相结合的滑坡灾害自动综合技术,并以滑坡灾害为例,构建了基于滑坡灾害程度区划、地形坡度、地貌区划、地震长期烈度区划、年暴雨日数、年均降水量等为约束图层的自动综合应用;通过多尺度综合分析结果表明,中国存在三大重点滑坡区:即青藏高原东部斜坡带、黄土高原滑坡区和太行山东麓、巫山、武陵山脉一线滑坡带。本研究为多尺度、多图层约束下的自然灾害风险地图自动综合提供了一种有效途径,同时对不同区域尺度下的灾害风险管理提供了更高效、更准确的决策支持和技术支撑。     

Wikiometrics: a Wikipedia based ranking system

We present a new concept—Wikiometrics—the derivation of metrics and indicators from Wikipedia. Wikipedia provides an accurate representation of the real world due to its size, structure, editing policy and popularity. We demonstrate an innovative “mining” methodology, where different elements of Wikipedia – content, structure, editorial actions and reader reviews – are used to rank items in a manner which is by no means inferior to rankings produced by experts or other methods. We test our proposed method by applying it to two real-world ranking problems: top world universities and academic journals. Our proposed ranking methods were compared to leading and widely accepted benchmarks, and were found to be extremely correlative but with the advantage of the data being publically available.     

流形排序算法预测microRNA*

在已知microRNA(miRNA)较少的情况下,为了提高算法预测的准确性,提出一种基于流形排序的miR-NA预测算法。该算法采用加权图模型描述序列,使用置信传播分配排序分数,降低了算法的时间复杂度;算法根据大规模数据内部全局流形结构进行排序,提高了排序结果的准确性。在人类和按蚊全基因组范围内的实验证明,流形排序算法的预测效果优于传统的预测方法,可以作为预测miRNA的一个有效工具。     

基于节点连通性排序的虚拟网络映射算法

对当今云环境下的数据中心来说,以虚拟资源租赁的运营方式具有极大的灵活性,尤其是以虚拟网络为粒度的资源租赁能够为用户提供更好的个性化需求支持。虚拟网络映射问题是指依据用户资源需求,合理分配底层主机和网络资源。现有的虚拟网络映射算法大多是针对随机拓扑设计的通用算法,未针对数据中心拓扑结构进行优化,映射效率有很大提升空间。针对数据中心的结构特点,提出了一种基于节点连通性排序的虚拟网络映射算法BS-VNE算法。首先,设计了一种最大生成算法来对虚拟节点重要程度进行求解和排序。该算法不仅基于虚拟节点的带宽和连通度,还基于虚拟节点在整个虚拟网络中的连通性来进行节点连通性的计算,以获得更加合理的排序结果。然后,根据虚拟节点连通性排序结果利用离散粒子群优化算法求解虚拟网络的映射解。在求解过程中,引入了针对数据中心结构的物理网络拓扑启发式规则,并将其组合到粒子搜索过程中,以提高映射算法的收敛速度。仿真实验结果表明,与现有算法相比,本文提出的算法可以提高物理网络的收益/成本比和资源利用率。     

基于PageRank的页面排序改进算法

首先对PageRank算法进行了一般性介绍,研究了现有的基于链接结构的改进算法.在此基础上,指出PageRank算法给不同网页分配相同的Pagegank值影响了网页的排序质量,提出了一种基于多层分类技术的改进算法HCPR,并对PageRank和HCPR算法进行了相应测试和比较.实验结果表明,HCPR的排序结果比PageRank提高了约15.3%的相关度.     

一种基于联合特征约束的毛孔检测算法

在皮肤数码图像的毛孔检测中,受不均匀光照、斑点、油光、毛发等因素影响,常常出现错检。针对这种情况,提出了一种基于联合特征约束的毛孔检测算法,算法充分考虑各影响因素,通过背景抑制、形态学处理削弱了光照不均和背景复杂的影响。基于毛孔色调特征,改进传统大津法计算最佳毛孔分割阈值,利用阈值进行分块分割。基于毛孔的尺寸、形状特征,对分割后的候选目标进行精细筛选,从而得到精确的毛孔检测结果。实验结果表明,提出的算法检测精度高,误检率低,具备良好的鲁棒性。     

Anticipation based on constraint processing in a multi-agent context

Anticipation is a general concept used and applied in various domains. Many studies in the field of artificial intelligence have investigated the capacity for anticipation. In this article, we focus on the use of anticipation in multi-agent coordination, particularly preventive anticipation which consists of anticipating undesirable future situations in order to avoid them. We propose to use constraint processing to formalize preventive anticipation in the context of multi-agent coordination. The resulting algorithm allows any action that may induce an undesirable future state to be detected upstream of any multi-agent coordination process. Our proposed method is instantiated in a road traffic simulation tool. For the specific question of simulating traffic at road junctions, our results show that taking anticipation into account allows globally realistic behaviors to be reproduced without provoking gridlock between the simulated vehicles.     

Folkommender: a group recommender system based on a graph-based ranking algorithm

With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a new graph-based approach to a recommender system, called Folkommender, that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on two real-world datasets demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.     

基于时间感知排序的云服务QoS预测方法研究

随着云计算理论和技术的成熟,越来越多的云服务得到了蓬勃发展,如何建立高质量的云服务成为了云计算研究领域的一个关键难题。服务质量QoS排序为用户从一系列功能相似的云服务候选者中挑选最优云服务提供了非常有价值的信息。为了获得云服务的QoS值,就需要调用真实的候选云服务。为了避免时间消耗和昂贵的资源浪费,提出了一种基于时间感知排序的云服务QoS预测方法。不同于传统的QoS值预测,基于QoS排序相似度的预测考虑为特定用户检测服务的排序。分时段按权计算出排序相似度,结合时间偏好合成相似度的前k位用户,用来提供信息支持QoS的缺失预测。在WS Dream真实数据集进行的实验研究表明,基于时间感知排序的云服务QoS预测方法有更好的预测精度。     

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.     

基于pairwise的改进ranking算法

传统基于pairwise的ranking算法，学习后得到的模型在用NDCG这样的ranking标准评价时效果并不好，对此提出了一种新型ranking算法。该算法也是使用样本对作为训练数据，但定义了一个面向NDCG评估标准的目标函数。针对此目标函数非平滑、难以直接优化的特点，提出使用割平面算法进行学习，不仅解决了上述问题，而且使算法迭代的次数不再依赖于训练样本对数。最后基于基准数据集的实验证明了算法的有效性。     

Recommender systems based on ranking performance optimization

The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization (MF) demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing models focus on the prediction of the users’ ratings on unknown items. The performance is usually evaluated by the metric root mean square error (RMSE). However, achieving good performance in terms of RMSE does not always guarantee a good ranking performance. Therefore, in this paper, we advocate to treat the recommendation as a ranking problem. Normalized discounted cumulative gain (NDCG) is chosen as the optimization target when evaluating the ranking accuracy. Specifically, we present three ranking-oriented recommender algorithms, NSMF, AdaMF and AdaNSMF. NSMF builds a NDCG approximated loss function for Matrix Factorization. AdaMF is based on an algorithm by adaptively combining component MF recommenders with boosting method. To combine the advantages of both algorithms, we propose AdaNSMF, which is a hybird of NSMF and AdaMF, and show the superiority in both ranking accuracy and model generalization. In addition, we compare our proposed approaches with the state-of-the-art recommendation algorithms. The comparison studies confirm the advantage of our proposed approaches.     

Listwise approaches based on feature ranking discovery

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.