Learning decision tree for ranking

Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms. In this paper, we present two novel class probability estimation algorithms to improve the ranking performance of decision tree. Instead of estimating the probability of class membership using simple voting at the leaf where the test instance falls into, our algorithms use similarity-weighted voting and naive Bayes. We design empirical experiments to verify that our new algorithms significantly outperform the recent decision tree ranking algorithm C4.4 in terms of AUC.

智能投票系统的UML建模

简要介绍了如何运用UML建模技术来设计智能投票系统,给出了系统的结构和主要功能,并举例就其中的主要模块详细介绍了UML建模。成功实现的智能投票系统验证了该模型的正确性。模型的建立过程对于同类软件的开发具有参考价值。     

Voting techniques for expert search

In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques. Extended version of ‘Voting for candidates: adapting data fusion techniques for an expert search task’. C. Macdonald and I. Ounis. In Proceedings of ACM CIKM 2006, Arlington, VA. 2006. doi: 10.1145/1183614.1183671.     

Application of wrapper approach and composite classifier to the stock trend prediction

The research on the stock market prediction has been more popular in recent years. Numerous researchers tried to predict the immediate future stock prices or indices based on technical indices with various mathematical models and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and ARIMA models. Although some researches in the literature exhibit satisfactory prediction achievement when the average percentage error and root mean square error are used as the performance metrics, the prediction accuracy of whether stock market goes or down is seldom analyzed. This paper employs wrapper approach to select the optimal feature subset from original feature set composed of 23 technical indices and then uses voting scheme that combines different classification algorithms to predict the trend in Korea and Taiwan stock markets. Experimental result shows that wrapper approach can achieve better performance than the commonly used feature filters, such as χ²-Statistic, Information gain, ReliefF, Symmetrical uncertainty and CFS. Moreover, the proposed voting scheme outperforms single classifier such as SVM, kth nearest neighbor, back-propagation neural network, decision tree, and logistic regression.     

Exploring a Hybrid Voting-based Eviction Policy for Caches and Sparse Directories on Manycore Architectures

In manycore systems, eviction decisions related to caches and memory coherence greatly impact system performance, thereby emphasizing their importance. Extensive research has produced numerous standalone eviction policies such as LRU, LFU, FIFO, etc. all aiming to attain the Bélády optimum solution. Standalone eviction policies optimize for a single attribute (recency, frequency, etc.), limiting their impact on applications exhibiting non-uniform memory access patterns. The Hybrid Voting-based Eviction Policy (HyVE) extends multiple standalone eviction policies with a ranking system and evaluates them using concepts from the voting theory domain. The goal of HyVE is to make better replacement decisions by creating a consensus among its constituent eviction policies. With its inherent voting properties, HyVE takes different replacement decisions compared to its standalone counterparts, making it a unique and new eviction policy. We deploy and evaluate HyVE as part of two case-studies: last-level cache replacement decisions in a generic manycore environment, and sparse directory eviction decisions on a tile-based distributed shared memory (DSM) architecture. We explore different variants of HyVE, and evaluate them using workloads from the PARSEC and SPLASH-2 benchmark suites in a simulation environment. We also compare HyVE to state-of-the-art set-dueling and learning-based eviction policies. For last-level cache replacement decisions, HyVE reduces cache misses by 7.4% compared to the LRU policy, whereas DRRIP and Hawkeye reduce cache misses by 5.5% and 9.2% respectively compared to the LRU policy. Though Hawkeye exhibits better performance on average, HyVE offers a unique advantage for certain workloads by using a voting-based approach to solve the replacement problem. For sparse directory eviction decisions, results show that HyVE reduces coherence traffic and execution time by up to 11% compared to the LRU policy. We have synthesized HyVE on an FPGA prototype. Hardware analysis results show that HyVE’s constituent policies contribute the most to its overheads, while HyVE’s ranking and voting extensions do not add significant overheads. Timing analysis results show that HyVE’s logic delay is comparable to that of standalone eviction policies. Lastly, we evaluate HyVE on the FPGA prototype using characteristic micro-benchmarks that further emphasize HyVE’s ability to remain agnostic to varying data access patterns.     

无线调度网与判选接收、同步发射技术

本文首先介绍了无线调度网的概念及其面临的问题 ,引出了判选接收、同步发射调度网的概念 ,并对判选接收、同步发射等技术进行了理论分析     

一种基于多图的集成直推分类方法

基于图的直推分类器依赖于图结构。高维数据通常具有冗余和噪声特征,在其上构造的图不能充分反映数据的分布信息,分类器性能因此下降。为此,该文提出一种多图构建方法并把它应用到直推分类中。该方法首先生成多个随机子空间并在每个子空间上进行半监督判别分析,其次在每个判别子空间上构造图并训练一个直推分类器,最后投票融合这些分类器为一个集成分类器。实验结果表明,对比其它直推分类器,该文的集成分类器具有分类正确率高、对参数鲁棒等特点。     

基于图像分解和区域分割的数字图像修复

提出一种基于图像分解和区域分割的图像修复新算法.首先,将图像分解为结构图像和纹理图像,然后根据分解的结构图像将其分割为不同区域,两区域间的边界线采用张量选举算法平滑连接;对各区域的结构和纹理图像分别采用基于紧支径向基甬数算法和自适应纹理匹配算法进行修复.最后将结构和纹理图像重新叠加在一起得到修复后的图像.该算法的优点是对图像的结构和纹理同时进行处理,实现对破损区域较大的图像进行有效修复;算法采用基于张量选举的区域边界连接和分区域修复,克服了单独使用径向基函数修复结构时产生边界模糊现象,采用的支径向基函数比普通的径向基函数具有较低的计算复杂度;丢失的纹理只在其所在区域内进行最优匹配搜索,大大减小了纹理搜索范围;纹理匹配块的自适应选择提高了纹理匹配的灵活性和准确性.实验证明,该算法能够稳定有效地处理各种较大的破损区域,并得到良好的图像修复效果.     

基于小波变换的语音基频提取新算法

该文将小波变换应用于具有连续语音特征的三字词语音的基频提取,并针对实验中出现的问题对算法进行了改进,提出了一种新的基于小波变换的语音基频检测算法。该算法主要包括:离散小波变换计算、基于投票策略的基频点选择和基频起点确定、基频检查、异常点修正、头尾漏点处理以及基于投票策略的基频点精确定位。实验表明,该算法较好地克服了基于小波变换传统算法的不足,更适合于连续语音的基频提取,缺陷是需要较多的计算时间,不太适合于实时性要求较高的系统。     

基于香农熵代表性特征和投票机制的三维模型分类

目前基于视图的3维模型分类方法存在单视图视觉信息不充分、多视图信息冗余的问题,且同等对待所有视图会忽略不同投影视角之间的差异性。针对上述问题,该文提出一种基于香农熵代表性特征和投票机制的3维模型分类方法。首先,通过在3维模型周围均匀设置多个视角组来获取表征模型的多组视图集。为了有效提取视图深层特征,在特征提取网络中引入通道注意力机制;然后,针对Softmax函数输出的视图判别性特征,使用香农熵来选择代表性特征,从而避免多视图特征冗余;最后,基于多个视角组的代表性特征利用投票机制来完成3维模型分类。实验表明：该方法在3维模型数据集ModelNet10上的分类准确率达到96.48%,分类性能突出。