Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.     

Attribute Generation Based on Association Rules

A decision tree is considered to be appropriate (1) if the tree can classify the unseen data accurately, and (2) if the size of the tree is small. One of the approaches to induce such a good decision tree is to add new attributes and their values to enhance the expressiveness of the training data at the data pre-processing stage. There are many existing methods for attribute extraction and construction, but constructing new attributes is still an art. These methods are very time consuming, and some of them need a priori knowledge of the data domain. They are not suitable for data mining dealing with large volumes of data. We propose a novel approach that the knowledge on attributes relevant to the class is extracted as association rules from the training data. The new attributes and the values are generated from the association rules among the originally given attributes. We elaborate on the method and investigate its feature. The effectiveness of our approach is demonstrated through some experiments. Received 6 December 1999 / Revised 28 October 2000 / Accepted in revised form 9 March 2001     

基于粗糙集及RBF网络的英文字母识别

将粗糙集理论与神经网络相结合,针对7×5分辨率的大写英文字母,构建了基于RBF网络的字母识别系统,给出了该识别系统的核心算法与核心结构.该系统利用粗糙集中最小决策算法对识别矩阵进行属性约简,减少了大量的计算和数据库存储量,同时提高了系统识别速度和识别率.通过计算机模拟实验,将该识别系统的识别率与标准BP网络算法及改进BP网络算法相比较,证实了该系统的优越性,在有约1/7的像素点受到随机干扰的情况下,该系统识别率仍可达到88%以上.     

Composing Stack-Attributed Tree Transducers

Stack-attributed tree transducers extend attributed tree transducers with a pushdown stack device for attribute values, which make them strictly more powerful. This paper presents an algorithm for the composition of stack-attributed tree transducers with attributed tree transducers. The algorithm is an extension of the existing method to compose attributed tree transducers. It leads to some natural closure properties of the corresponding classes of tree transformations.     

CCERT信息服务的访问控制模型及其实现

CCERT信息服务的访问控制模型是一种新型的基于属性证书的分布式系统的访问控制模型。文章在分析基于X．509公钥证书的访问控制模型的基础上,通过引入属性证书的概念,结合CCERT分布式信息服务系统的特点,给出了CCERT信息服务的访问控制模型以及该模型的具体实现。     

GIS数据之属性不确定性的研究

GIS中的不确定性可分为位置,属性,时域,逻辑关系和完整性等几个方面,属性数据的不确定将直接影响基于GIS决策的准确性和可靠性,特别是对侧重于属性分析的领域,本文首先总结了属性不确定的含义,来源,传播,它和位置不确定性的关系,以及属性不确定性分析和可视化的理论和方法,最后概述了属性不确定性的研究进展。     

基于小波神经网络的多属性决策方法

在分析了多属性决策问题及现有方法之后,提出了用小波神经网络建立权重分配模型从而改进多属性决策的方法,使权重的确定较为客观准确,并具有自学习功能,还介绍了该方法在工业产品质量决策方案评价中的应用.     

一种Agent结构的形式化模型

从外表看,Ａｇｅｎｔ是具有信念、意向、期望等意识属性的统一整体。Ａｇｅｎｔ之间可相互通信或相互合作,而且,Ａｇｅｎｔ自身还可以有诸如计算,推理或其他人们所希望的具有一定的智能特征的行为。为了把Ａｇｅｎｔ行为和它所应具有的内部结构联合起来,本文提出一种Ａｇｅｎｔ 形式化描述模型,在此模型的帮助下,可以较好地把一些Ａｇｅｎｔ的简单行为解释为Ａｇｅｎｔ内部状态的演变过程。这对Ａｇｅｎｔ的建模与实现,加深     

Ranking Fuzzy Cognitive Map based scenarios using ELECTRE III: Applied on housing market

Policy makers often deal with a wide range of alternative probable future states for the entity they work for – a country, for example. To strive for the most desirable state, the policy makers must evaluate and rank all probable future scenarios. To this end, ‘scenario methods’ gained recent popularity are increasingly being employed. Nevertheless, currently, decisions made based on insight gained from scenarios are not made in an integrated systematic process. Current variants of the method help study the role of any research concept individually; thus, they do not provide a complete picture of the research situation. A more suitable variant of the method for today's world should provide a holistic view of the research situation by modelling possible links among research concepts. This paper, introduces a stepwise methodology that can guide the building, developing, and ranking of possible future scenarios, having factored in the possible causal interrelations among research concepts. The method is enriched with a combination of Fuzzy Cognitive Maps, a widely used soft computing method, and ELECTRE III, a popular method of Multi Attribute Decision Making. This paper also presents the results of the application of the proposed methodology for Iran's housing market, highlighting the advantages of the proposed methodology.     

基于微博的用户相似度计算研究

针对传统的相似度计算方法和评价标准在准确、高效地度量微博用户相似关系时不理想的缺陷,提出一种新的微博用户相似度的计算方法。该方法针对不同的属性数据结构采用不同的计算方式,并根据属性统计信息对各个属性赋值,利用层次分析法确定各属性权值,最后构建综合相似度计算模型。实验结果表明,相对于传统的相似度计算方法,所提计算方法衡量用户相似的准确度提高了22.6%,召回率提高了12.7%,F1度量值提高了29.5%。