Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.     

Accelerating filtering techniques for numeric CSPs

Search algorithms for solving Numeric CSPs (Constraint Satisfaction Problems) make an extensive use of filtering techniques. In this paper¹ we show how those filtering techniques can be accelerated by discovering and exploiting some regularities during the filtering process. Two kinds of regularities are discussed, cyclic phenomena in the propagation queue and numeric regularities of the domains of the variables. We also present in this paper an attempt to unify numeric CSPs solving methods from two distinct communities, that of CSP in artificial intelligence, and that of interval analysis.     

基于卷积树核的中文微博情感要素识别

情感要素识别是情感分析的关键子任务之一,其目的是识别出文本情感所作用的情感对象。文本情感要素识别属于最细粒度的情感分析,吸引了大量研究者的关注。中文微博由于其语言简短灵活、文本不规范、噪声较大等特点,给中文微博情感分析研究工作带来了新的挑战。目前大部分情感要素识别方法都是基于规则的方法或者基于扁平化特征的统计学习方法,区分噪声的能力不强,性能提升有限。针对中文微博的特点,提出一种基于卷积树核的情感要素识别算法,即首先对句子进行词性标注与依存关系分析,将句子中的名词作为候选情感要素;然后基于两种不同的修剪策略对依存树进行修剪,以获取每个候选情感要素的结构化信息;最后采用卷积树核计算依存树的相似度,并在此基础上识别句子中的情感要素。NLPCC2012和NLPCC2013中文微博情感分析评测任务中的实验验证了该方法的性能,其准确率相比于传统方法有显著提升。     

Effective solution for unhandled exception in decision tree induction algorithms

This paper deals with some improvements to rule induction algorithms in order to resolve the tie that appear in special cases during the rule generation procedure for specific training data sets. These improvements are demonstrated by experimental results on various data sets. The tie occurs in decision tree induction algorithm when the class prediction at a leaf node cannot be determined by majority voting. When there is a conflict in the leaf node, we need to find the source and the solution to the problem. In this paper, we propose to calculate the Influence factor for each attribute and an update procedure to the decision tree has been suggested to deal with the problem and provide subsequent rectification steps.     

逻辑语言剪枝算子的过程语义及其实现

论述了逻辑程序设计中剪枝算子的作用及传统剪枝算子的过程性语义和说明性语义不一致问题;介绍了新型逻辑程序语言〔淑划中的COTRTT11t剪枝算子;通过引入一组定义描述其过程语义,并进一步阐述了剪枝算子和延迟计算规则之间的关系,讨论了Godel语言的剪枝策略及控制机制,从而为逻辑程序语言的实现提供了依据。     

Resolving permutation ambiguity in correlation-based blind image separation

We address the problem of permutation ambiguity in blind separation of multiple mixtures of multiple images (resulting, for instance, from multiple reflections through a thick grass plate or through two overlapping glass plates) with unknown mixing coefficients. In this paper, first we devise a generalized multiple correlation measure between one gray image and a set of multiple gray images and derive a decorrelation-based blind image separation algorithm. However, many blind image separation methods, including this algorithm, suffer from a permutation ambiguity problem that the success of the separation depends upon the selection of permutations corresponding to the orders of the update operations. To solve the problem, we improve the first algorithm above by decorrelating the mixtures while searching for the appropriate update permutation using a pruning technique. We show its effectiveness through experiments with artificially mixed images and real images.     

基于“断弦护枝”思想的MST构造算法的设计与分析

为弥补MS I’经典算法的局限性,开创性地提出了构造MST的“断弦护枝”思想,并基于此思想创造性地设计与实现了一种全新的MST构造算法。实验结果和数学证明表明新算法是正确的;实验和分析结果表明新算法在某些实际应用领域能显著弥补经典算法的不足,具有重要的理论和应用价值。     

规则简化与模糊决策树剪枝的比较

决策树归纳学习算法是机器学习领域中解决分类问题的最有效工具之一。由于决策树算法自身的缺陷了，因此需要进行相应的简化来提高预测精度。模糊决策树算法是对决策树算法的一种改进，它更加接近人的思维方式。文章通过实验分析了模糊决策树、规则简化与模糊规则简化；模糊决策树与模糊预剪枝算法的异同，对决策树的大小、算法的训练准确率与测试准确率进行比较，分析了模糊决策树的性能，为改进该算法提供了一些有益的线索。     

A <Emphasis Type="Italic">k</Emphasis>-norm pruning algorithm for decision tree classifiers based on error rate estimation

Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone’s Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Editor: Hendrik Blockeel.     

有约束的一维装箱问题的新型算法设计

在工业生产中经常遇到材料切割问题,如何给出材料利用率最高或接近最高的切割方案是一个有意义的工作.通过分析,融合多种算法,设计出了一个行之有效的优化算法,通过实际测试,证明材料利用率为98.7%以上.