SVM for English semantic classification in parallel environment

Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set.     

A valences-totaling model for English sentiment classification

Sentiment classification plays an important role in everyday life, in political activities, activities of commodity production and commercial activities. Finding a time-effective and highly accurate solution to the classification of emotions is challenging. Today, there are many models (or methods) to classify the sentiment of documents. Sentiment classification has been studied for many years and is used widely in many different fields. We propose a new model, which is called the valences-totaling model (VTM), by using cosine measure (CM) to classify the sentiment of English documents. VTM is a new model for English sentiment classification. In this study, CM is a measure of similarity between two words and is used to calculate the valence (and polarity) of English semantic lexicons. We prove that CM is able to identify the sentiment valence and the sentiment polarity of the English sentiment lexicons online in combination with the Google search engine with AND operator and OR operator. VTM uses many English semantic lexicons. These English sentiment lexicons are calculated online and are based on the Internet. We present a full range of English sentences; thus, the emotion expressed in the English text is classified with more precision. Our new model is not dependent on a special domain and training data set—it is a domain-independent classifier. We test our new model on the Internet data in English. The calculated valence (and polarity) of English semantic words in this model is based on many documents on millions of English Web sites and English social networks.     

基于知识的模型自动选择策略

模型自动选择是决策支持系统智能化发展的必然要求。针对目前实用算法较少的现状,提出一种模型自动选择策略。基于知识框架描述模型,根据事实库和知识库提取相应规则生成推理树,结合经验和专业知识实现模型自动选择。实验结果表明,该策略具有较高的命中率。     

基于粗糙集的决策树构造算法

针对ID3算法构造决策树复杂、分类效率不高问题,基于粗糙集理论提出一种决策树构造算法。该算法采用加权分类粗糙度作为节点选择属性的启发函数,与信息增益相比,能全面地刻画属性分类的综合贡献能力,并且计算简单。为消除噪声对选择属性和生成叶节点的影响,利用变精度粗糙集模型对该算法进行优化。实验结果表明,该算法构造的决策树在规模与分类效率上均优于ID3算法。     

决策树算法的一种改进算法

决策树是归纳学习和数据挖掘的重要方法,主要用于分类和预测.ID3算法是决策树中应用最广泛的算法,通过对数据挖掘中决策树的基本思想进行阐述,讨论了ID3算法倾向于取值较多属性的缺点,引入无关度对ID3算法作了改进.实验数据结果分析表明,改进后的算法能得到更合理、更有效的规则.     

基于粗糙集分类算法研究与实现

数据挖掘是人工智能中知识发现的重要组成部分,而分类又是一种主要的应用形式。ID3算法是数据挖掘中经典的决策树分类算法,ID3算法具有抗噪声能力差的缺点。通过对分类和粗糙集理论的研究,将可变精度粗糙集理论的思想应用在计算属性信息熵时设定阈值上,以放宽属性选择的要求,从而对经典的ID3算法作了相应的改进。改进后的ID3算法（称之为VPID3算法）可在一定程度上降低噪声对系统分类的干扰,提高了有数据有噪声情况下的分类精度。另外根据该算法设计并实现了一个分类器,并通过实验检验了该算法的性能。     

基于属性重要度的决策树算法

传统的ID3决策树算法存在属性选择困难、分类效率不高、抗噪性能不强、难以适应大规模数据集等问题。针对该情况,提出一种基于属性重要度及变精度粗糙集的决策树算法,在去除噪声数据的同时保证了决策树的规模不会太庞大。利用多个UCI标准数据集对该算法进行了验证,实验结果表明该算法在所得决策树的规模和分类精度上均优于ID3算法。     

A fuzzy ID3 induction for linguistic data sets

In real life, humans communicate by means of words. Computing with words enables flexibility via fuzzy logic to reach more informative results for the classification and decision‐making. Fuzzy logic handles the imprecise information. In our paper, we propose a novel fuzzy ID3 algorithm for the classification on linguistic data set, where data can be given as linguistic variables. Linguistic variables are defined by using triangular fuzzy numbers given as LR (left‐right) fuzzy numbers. And weighted averaging based on levels (WABL) method is used as the defuzzification method for each data. Then, fuzzy c‐means algorithm is performed to handle the membership degrees for each variable given in each data set used in an experimental study. At last, the fuzzy ID3 algorithm is applied. The rules are generated, and the reasoning is done by different T‐operators. Our study is encouraged by (using) statistical analysis. In conclusion, it is seen that our algorithm proposed for linguistic data is as good as the proposed approach for numeric data. Also, it is shown that the proposed linguistic approach by using different T‐operators on linguistic data gives better results than numerical approach on some data sets.     

一种基于灰色关联度的决策树改进算法

在构造决策树的过程中,分裂属性选择的标准直接影响分类的效果。分析了现有改进的ID3算法不同程度地存在学习效率偏低和对多值属性重要性的主观评测等问题,提出一种高效而且可靠的基于灰色关联度的决策树改进算法。该算法通过灰色关联分析建立各特征属性与类别属性之间的关系,进而利用灰色关联度来修正取值较多但非重要属性的信息增益。通过实验与其它ID3改进算法进行了比较,验证了改进后的算法是有效的。     

A simple decomposition algorithm for support vector machines with polynomial-time convergence

Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role.So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence.In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence.     

一种多变量决策树的构造与研究

单变量决策树算法造成树的规模庞大、规则复杂、不易理解,而多变量决策树是一种有效用于分类的数据挖掘方法,构造的关键是根据属性之间的相关性选择合适的属性组合构成一个新的属性作为节点。结合粗糙集原理中的知识依赖性度量和信息系统中条件属性集的离散度概念,提出了一种多变量决策树的构造算法（RD）。在UCI上部分数据集的实验结果表明,提出的多变量决策树算法的分类效果与传统的ID3算法以及基于核方法的多变量决策树的分类效果相比,有一定的提高。     

改进的决策树算法在文理分科中的应用研究

文理分科是高中生面临的第一次重大选择,选文科还是理科,很多同学感到两头难。针对这种情况,通过对比决策树分类算法中的ID3和C4.5算法,提出了基于影响因子的新的分类算法,构造了"文理分科分类器"。实验证明该方法在文理分科问题上比传统的ID3和C4.5算法有更高的分类精确度,该分类器可以辅助学生和家长进行文理科的选择,降低选择的错误性。     

变精度粗糙集模型在决策树构造中的应用

针对ID3算法构造决策树复杂、分类效率不高等问题,本文基于变精度粗糙集模型提出了一种新的决策树构造算法。该算法采用加权分类粗糙度作为节点选择属性的启发函数,与信息增益相比,该标准更能够全面地刻画属性分类的综合贡献能力,计算简单,并且可以消除噪声数据对选择属性和生成叶节点的影响。实验结果证明,本算法构造的决策树在规模与分类效率上均优于ID3算法。     

基于MapReduce的决策树算法并行化

针对传统决策树算法不能解决海量数据挖掘以及ID3算法的多值偏向问题,设计和实现了一种基于MapReduce架构的并行决策树分类算法。该算法采用属性相似度作为测试属性的选择标准来避免ID3算法的多值偏向问题,采用MapReduce模型来解决海量数据挖掘问题。在用普通PC搭建的Hadoop集群的实验结果表明:基于MapReduce的决策树算法可以处理大规模数据的分类问题,具有较好的可扩展性,在保证分类正确率的情况下能获得接近线性的加速比。     

Rough set based hybrid algorithm for text classification

Automatic classification of text documents, one of essential techniques for Web mining, has always been a hot topic due to the explosive growth of digital documents available on-line. In text classification community, k-nearest neighbor (kNN) is a simple and yet effective classifier. However, as being a lazy learning method without premodelling, kNN has a high cost to classify new documents when training set is large. Rocchio algorithm is another well-known and widely used technique for text classification. One drawback of the Rocchio classifier is that it restricts the hypothesis space to the set of linear separable hyperplane regions. When the data does not fit its underlying assumption well, Rocchio classifier suffers. In this paper, a hybrid algorithm based on variable precision rough set is proposed to combine the strength of both kNN and Rocchio techniques and overcome their weaknesses. An experimental evaluation of different methods is carried out on two common text corpora, i.e., the Reuters-21578 collection and the 20-newsgroup collection. The experimental results indicate that the novel algorithm achieves significant performance improvement.     

邻域等价关系诱导的改进ID3决策树算法

经典ID3决策树算法适用于离散型数据分类,但用于连续处理时需要数据离散化容易导致信息损失。提出邻域等价关系从而诱导邻域ID3(NID3)决策树算法,NID3算法改进了ID3决策树算法,能够直接实施连续预测并获取更好的分类效果。在邻域决策系统中,挖掘一种邻域等价关系;基于邻域等价粒化,构建邻域信息度量;基于邻域信息增益,设计NID3决策树算法。实例分析与数据实验均表明,NID3算法具有连续数据分类预测有效性,在分类机器学习中优于ID3算法。     

基于可变精度的ID3改进算法

ID3算法是数据挖掘中经典的决策树分类算法，该算法具有抗噪声能力差的缺点。通过对ID3算法的研究，依据可变精度粗糙集理论的思想，采用在计算属性信息熵时设定阈值的方法，以放宽属性选择的要求，从而对经典的ID3算法做了相应的改进。改进后的ID3算法（VPID3）可在一定程度上降低噪声对系统分类的干扰，使分类结果更加符合实际要求。最后通过举例，说明了改进算法的可行性。     

基于Rough集潜在语义索引的Web文档分类

Rough集(粗糙集)埋论是一种处理不确定或模糊知识的数学工具。提出了一种基于Rough集理论的潜在语义索引的Web文档分类方法。首先应用向量空间模型表示Web文档信息，然后通过矩阵的奇异值分解来进行信息过滤和潜在语义索引；运用属性约简算法生成分类规则，最后利用多知识库进行文档分类。通过试验比较，该方法具有较好的分类效果。     

一种基于属性敏感度的决策树算法

决策树算法是数据挖掘中重要的分类算法。目前,已有许多构建决策树的算法,其中,ID3算法是核心算法。本文首先对ID3算法进行研究与分析,针对计算属性的信息熵十分复杂的缺点,提出了一种新的启发式算法SID3,它是基于属性对分类的敏感度的。文章最后通过实例对两种算法进行比较分析,结果表明,SID3算法能够生成正确的决策树,并且使建树过程更简便,更快速。     

基于分类矩阵的决策树算法

为了提高决策树分类的速度和精确率,提出了一种基于分类矩阵的决策树算法.介绍了ID3算法的理论基础,定义了一种分类矩阵,指出了ID3算法的取值偏向性并利用分类矩阵给出了证明.在此基础上,引入了一个权重因子,抑制了原有算法的取值偏向,并利用分类矩阵给出相应证明,同时根据基于分类矩阵增益的特点,提出了新的决策树分类方案,旨在运算速率上进行优化,与原有算法进行了实验比较.对实验结果分析表明,优化后的方案在性能上有明显改善.