ContentRank:一种新颖的产品页面排名算法

提出了一种新颖的ContentRank算法实现对产品页面的排名.算法不仅考虑了产品页面的链接结构,还对与产品相关的用户评论进行分类与分析,最终计算出各产品页面的得分并排名.实验结果表明,与Page-Rank相比,该算法可获得更好的排名结果,提高用户对搜索结果的满意度.     

一种基于页面内容与关联性的网页排名技术

排名在信息检索领域是一项非常重要的研究课题。然而目前研究页面排名的算法都仅关注页面自身的内容,而并未考虑页面之间的关联性,但这种页面间的关联是普遍存在的。提出了一种新颖的基于页面内容与关联性的排名算法,这种方法既考虑了页面自身的结构特征,又考虑了页面之间的关联性。算法的提出成功地解决了信息检索领域中的“主题蒸馏”问题。为了验证算法的有效性,还在标准的信息检索数据集上进行了对比实验,实验表明算法性能优于传统的排名算法。     

基于分类技术的搜索引擎排名算法--CategoryRank

提出了一种基于分类技术的搜索引擎新排名算法CategoryRank。该算法能够借助类别信息,更加准确地计算网页的排名得分,提高搜索引擎排名的准确性。算法基于任意两个网页之间的类别信息,对链接图进行了分析和计算,并且与PageRank等算法进行相比,该算法能够更加准确地模拟用户浏览网页的习惯。同时针对Web中的每个网页,算法计算出它的类别属性,直接体现了该页面针对不同用户的重要程度。最后,把该算法的离线模型扣在线模型统一起来,阐明了算法在搜索引擎排名中的运行机制。     

一种改进的自适应页面置换算法

研究缓冲区页面置换策略和算法(特别是自适应页面置换策略和算法),提出一种基于双管理链的自适应页面置换算法HA。HA算法是对DMC(2c)算法的改进,它引入动态置换点,同时,根据缺页失败数确定算法的工作链,并根据页面访问序列的局部特征选择效率较高的页面置换策略。实验结果表明,HA算法能有效地减少缺页失败数,降低缺页率,特别是在处理第三种模式的页面访问序列时,该算法的缺页率较改进前的算法可降低近30%。     

基于Web页面结构和主色调的聚类算法

针对目前Web聚类准确率不高的问题,提出一种基于Web页面链接结构和页面中图片主色调特征的聚类算法。通过分析Web页面中的链接结构和Web页面中所显示图片的主色调来比较页面之间的相似度,对Web站点中的Web页面进行聚类。聚类过程兼顾Web页面结构和页面的主要色彩特征。系统实验结果表明,该算法能有效提高聚类的准确性。     

网络钓鱼Web页面检测算法

网络钓鱼(Phishing)攻击在电子商务和电子金融中普遍存在。该文分析Phishing页面敏感特征,提出一种防御Phishing攻击的Web页面检测算法。该算法通过分析Web页面的文档对象模型来提取Phishing敏感特征,使用BP神经网络检测页面异常程度,利用线性分类器判断该页面是否为Phishing页面。该算法成功过滤了Phishing页面,有效地阻止了Phishing攻击。     

连通区的页面分割与分类方法

页面分割与分类是文档处理的关键步骤，但目前多数方法对页面的块和倾斜进行了限制，文中提出一种新的基于连通区的页面分割与分类方法，首行采用快速算法抽取页面内的连通区，然后利用改进的PLSA算法分割页面，并根据连通区的分布情况以及块的特征对块进行分类，该方法页面分割与分类紧密结合，充分考虑到块的局部特征，保证块分类的正确性，大大提高了算法效率。     

基于分布式学习自动机和用户反馈的网页排序算法研究

为了改善传统PageRank算法存在的不足,例如平分链接权重、主题漂移和忽略用户兴趣,提出一种基于分布式学习自动机和用户反馈的网页排序算法。利用页面内容的相似性、网页之间的超链接和用户遍历的路径,根据分布式学习自动机来确定网页间的超链接权重。考虑到用户反馈包含大量的价值信息,选择用户的转载、回复以及有效点击特征作为用户的行为特征,获得用户反馈因子。根据网页间的超链接权重和用户反馈因子计算每个网页的排名。仿真实验表明,与传统的PageRank算法和WPR算法相比,该算法在一定程度上提高了信息检索的精准度和用户满意度。     

图形化程序特征码的算法设计与应用

提出一种图形化页面程序特征码的提取算法.该算法根据图形化页面内符号间输入输出数据流关系和数据类型信息,形成页面的中间信息文本.中间信息文本由该页面对外输入的输出变量名、变量类型,相关符号的输入、输出形参类型和参数设置值,输出变量的数据流调用表达式等关键内容拼接组成,基于中间信息文本计算形成CRC,作为页面的特征码.通过计算比较厂家内部修改页面、现场工程修改页面之间的特征码,给出页面程序修改是否一致的结果,该算法可准确甄别由于符号位置偏差、中间变量命名不同等原因导致的页面存储文件不同,但实际功能一致的情况.     

基于超链接多样性分析的新型网页排名算法

作为搜索引擎的核心部件,网页排名算法决定了搜索到的相关结果以何种顺序呈现给用户,其性能的优劣将会直接影响搜索引擎的服务质量和用户的搜索体验.在计算网页的权威性时,现有的基于链接的网页排名算法和网页作弊检测算法仅关注网页的超链接数量和质量,而忽略了超链接来源的多样性———另一种客观评价网页权威性的重要信息.相比于真正的权威页面(具有大量且来源广泛的入链),通过作弊手段提升排名的网页往往不具有入链来源多样性的特征.基于以上思想,文中分别提出了超链接来源多样性判断方法、超链接权值调整方法,进而提出了基于超链接来源多样性分析的网页排名算法Drank.在多个基准数据集上的实验结果表明:与现有最好的同类算法相比,综合寻找优质页面和抑制网页排名作弊两方面,Drank算法表现出更好的性能.     

面向排序学习的层次聚类特征选择算法

大型搜索系统对用户查询的快速响应尤为必要,同时在计算候选文档的特征相关性时,必须遵守严格的后端延迟约束。通过特征选择,提高了机器学习的效率。针对排序学习中快速特征选择的起点多为单一排序效果最好的特征的特点,首先提出了一种用层次聚类法生成特征选择起点的算法,并将该算法应用于已有的2种快速特征选择中。除此之外,还提出了一种充分利用聚类特征的新方法来处理特征选择。在2个标准数据集上的实验表明,该算法既可以在不影响精度的情况下获得较小的特征子集,也可以在中等子集上获得最佳的排序精度。     

一种面向标签排序数据集的特征选择方法

针对标签排序问题的特点,提出一种面向标签排序数据集的特征选择算法（Label Ranking Based Feature Selection, LRFS）。该算法首先基于邻域粗糙集定义了新的邻域信息测度,能直接度量连续型、离散型以及排序型特征间的相关性、冗余性和关联性。然后,在此基础上提出基于邻域关联权重因子的标签排序特征选择算法。实验结果表明,LRFS算法能够在不降低排序准确率的前提下,有效剔除标签排序数据集中的无关特征或冗余特征。     

基于互信息和遗传算法的两阶段特征选择方法

为了在特征选择过程中得到较优的特征子集,结合标准化互信息和遗传算法提出了一种新的两阶段特征选择方法。该方法首先采用标准化的互信息对特征进行排序,然后用排序在前的特征初始化第二阶段遗传算法的部分种群,使得遗传算法的初始种群中含有较好的搜索起点,从而遗传算法只需较少的进化代数就可搜寻到较优的特征子集。实验显示,所提出的特征选择方法在特征约简和分类等方面具有较好的效果。     

Unsupervised spectral feature selection algorithms for high dimensional data

It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data, especially for those with very small number of samples. Feature selection especially the unsupervised ones are the right way to deal with this challenge and realize the task. Therefore, two unsupervised spectral feature selection algorithms are proposed in this paper. They group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation, so as to detect the global optimal feature clusters as far as possible. Then two feature ranking techniques, including cosine-similarity-based feature ranking and entropy-based feature ranking, are proposed, so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be built. The effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods, and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer methods. The extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison, especially the one based on cosine similarity feature ranking technique. The statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs best. The detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data, such that the AI system built on them would be reliable and explainable. It is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.     

面向排序学习的锦标赛排序特征选择方法

排序问题在信息检索领域是一个非常重要的课题。虽然排序学习模型的算法早已被深入研究,但针对排序学习算法中的特征选择的研究却很少。现实的情况是,许多用于分类的特征选择方法被直接应用到排序学习中。但由于排序和分类有着显著的差异,应研究出针对排序的特征选择算法。文中在介绍常用的排序学习的特征选择方法的基础上,提出了一种全新的、适用于QA问题的排序学习的特征选择方法一锦标赛排序特征选择方法。实验结果显示,这种新的特征选择方法在提高特征提取效率和降低特征向量维数方面都有显著改善。     

Dynamic feature selection algorithm based on Q-learning mechanism

Feature selection is a technique to improve the classification accuracy of classifiers and a convenient data visualization method. As an incremental, task oriented, and model-free learning algorithm, Q-learning is suitable for feature selection, this study proposes a dynamic feature selection algorithm, which combines feature selection and Q-learning into a framework. First, the Q-learning is used to construct the discriminant functions for each class of the data. Next, the feature ranking is achieved according to the all discrimination functions vectors for each class of the data comprehensively, and the feature ranking is doing during the process of updating discriminant function vectors. Finally, experiments are designed to compare the performance of the proposed algorithm with four feature selection algorithms, the experimental results on the benchmark data set verify the effectiveness of the proposed algorithm, the classification performance of the proposed algorithm is better than the other feature selection algorithms, meanwhile the proposed algorithm also has good performance in removing the redundant features, and the experiments of the effect of learning rates on the our algorithm demonstrate that the selection of parameters in our algorithm is very simple.

     

A modified Intelligent Water Drops algorithm and its application to optimization problems

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

基于LambdaMART算法的微信公众号排序

随着移动应用的普及,微信公众号已经成为人们获取信息的重要来源之一。微信公众号排序是获取优质信息、节约信息管理成本的必要手段。现有的公众号排序方法主要是对总阅读数、总点赞数等量化指标进行人工经验赋权得到排序结果,忽略了文章内容对公众号选择的影响。该文在保留量化指标的基础上,提出了主题垂直性、发文稳定性、主题覆盖率和主题相关性等微信篇章排序特征,使用LambdaMART算法针对上述特征集合进行排序学习,并通过主成分分析进行特征选择优化。实验结果表明,在公众号排序方面,LambdaMART方法优于现有其他方法,相关实验也证明了基于微信篇章内容分析特征的有效性。