粗糙集理论在决策判断中的应用

属性约简是RoughSet理论研究中的核心内容之一,现在已经证明寻找决策表的最小约简是NP hard问题。本文首先阐述了可辨识矩阵的概念;然后在此基础上提出了一种基于可辨识矩阵和信息熵的属性约简的新算法,并给出了该算法的时间复杂度;最后,通过实例分析表明,本文算法能够成功用于决策判断,并且在多数情况下该算法能够得到决策表的最小约简。     

不完备信息系统中知识约简的一种启发式算法

不完备信息系统中的知识获取是粗集理论应用的难点。通过引入信息熵和条件信息熵，对信息系统中属性的必要性进行了定义；提出了一种基于条件信息熵的知识约简启发式算法，并指出该算法的时间复杂度是多项式的。通过实例说明，该算法能得到信息表的约简和决策表的最小相对约简。     

一种基于关系矩阵维度增量式约简算法

目前决策表的数据信息都是动态变化的,为了有效地对这些数据集进行属性约简处理,在改进的等价关系矩阵的基础上,提出一种增量式属性约简算法,决策表增加了一些属性,利用更新前决策表的等价关系矩阵和属性约简等信息,便能快速求解出更新后的决策表属性约简.实验结果进一步验证了该方法的有效性和正确性.     

基于量子蛙跳协同进化的粗糙属性快速约简

 属性约简是粗糙集理论研究的重要内容,现已证明求决策表最小约简是一个典型NP难题.本文提出一种基于量子蛙群协同进化的粗糙属性快速约简算法.该算法构造一种动态多簇的蛙群结构,用量子态比特进行蛙群个体编码,以自适应量子旋转角调整、量子变异和量子纠缠等策略加速蛙群进化收敛,各簇蛙群以双向协同学习机制共享属性约简中相关信息.标准Benchmark优化函数测试结果表明该算法在保证收敛速度同时具有较强的平衡全局优化与局部细致搜索能力.在UCI数据集上进行属性约简比较实验,结果验证了本算法在属性约简精度和效率方面具有明显优势.     

基于分明矩阵的序贯属性约简算法

根据决策表信息系统的分明矩阵及序贯思想,提出了序贯属性约简算法,该算法首先构造递增序列的分明函数,然后利用逐次增加的属性核对分明函数进行分支运算,并建立属性约简树,从而求出所有约简.该算法避免了大量的逻辑运算,实现了高维数据的高效属性约简.理论分析和实验结果表明该算法具有更高的运行效率.     

粗糙集理论在步战车故障诊断专家系统中知识自动获取的应用

根据粗糙集理论,实现步战车故障诊断专家系统知识自动获取。首先根据历史故障数据建立决策表,利用粗糙集数据挖掘算法对决策表进行属性约简,对得到的决策表消除冗余,再利用属性值约简算法对决策表进行属性值约简,最后得到决策规则。知识库由条件表、结论表和事实表组成,保存在Access2003中。把条件属性保存到知识库的条件表中,把决策属性保存到知识库的结论表中,这样就很方便的建立了知识库。     

决策表中基于条件信息熵的近似约简

属性约简是粗糙集理论的重要研究内容,已有效应用于机器学习、数据挖掘等领域.基于条件信息熵的属性约简可有效推广代数观下的属性约简,但存在抗噪声弱且某些情况下冗余属性多的不足.为此,本文在引入决策表中基于条件信息熵的近似约简概念后,提出决策表中基于条件信息熵的近似约简算法,该算法可有效增强抗噪性,且可依据实际应用的需要有效地对冗余属性进行取舍.最后,本文侧重通过选择不同精度下的约简属性子集在Benchmark上进行了分类器的性能测试.     

粗糙集理论中属性相对约简算法

粗糙集理论是近年来发展起来的一种有效地处理模糊和不确定性知识的数学工具,而求核与约简是粗糙集理论中的两个重要问题,现已证明求决策表所有约简和最小约简是一个典型的NP难题.本文在分析粗糙集理论的基础上,发现了正区域的一些有用性质,提出了一种利用正区域直接求核的方法,并利用正区域的启发式信息给出了两种相对约简算法.     

基于主曲线的脱机手写数字识别

该文提出了一种基于主曲线的脱机手写数字识别方法.该方法将主曲线及知识约简算法运用于识别模型中.主曲线是主成份分析的非线性推广,它是通过数据分布"中间"并满足"自相合"的光滑曲线.它较好地反映了数据分布的结构特征.粗糙集理论的知识约简是从决策表中获取决策(分类)规则的有效工具.本文将主曲线用于训练数据的特征提取,根据主曲线的特征生成决策表;利用我们提出的知识约简算法对决策表进行处理,自动获得分类规则.这种方法既符合人的识别习惯,又克服了利用统计特征识别所带来的不足.实验结果表明了该方法能有效提高手写数字的识别率,为脱机手写数字识别的研究提供了一条新途径.     

关于粒度熵的知识约简算法

针对现有知识约简算法中存在的不完备性问题,提出了一种基于粒度熵的启发式知识约简算法,将该算法应用于电力变压器故障诊断决策表的约简,避免了选择约简集的盲目性,同时也大大提高了故障诊断的效率。     

Application of information theory to the construction of efficient decision trees

The problem of conversion of decision tables to decision trees is treated. In most cases, the construction of optimal decision trees is an NP-complete problem and, therefore, a heuristic approach to this problem is necessary. In this heuristic approach, an application of information theoretic concepts to construct efficient decision trees for decision tables which may include "don't care" entries is made. In contrast to most of the existing heuristic algorithms, this algorithm is systematic and is intuitively appealing from an information theoretic standpoint. The algorithm has low design complexity and yet provides near-optimal decision trees.     

Heterogeneous Wireless Networks: Configuration and Vertical Handoff Management

Vertical handoff is one of the most important issues for the next generation heterogeneous wireless networks. However, in many situations, unbeneficial vertical handoffs occur across intersystem heterogeneous networks cause network performance degradation. Therefore, we propose a novel configuration architecture that can be deployed in the next generation of wireless networks. Second, we propose a predictive and adaptive Vertical Handoff Decision Scheme that optimizes the handoff initiation time as well as selection of the most optimal network. The proposed vertical handoff decision algorithm considers the technology type as well as the Signal to Interference Ratio (SIR), the Mobile Station (MS) velocity, the user preferences, the applications requirements and the terminal capabilities as the most important factors to make vertical handoff decision. In order to minimize handoff costs, the proposed decision algorithm uses the dwell timer concept. The handoff costs are analyzed in terms of unnecessary and unbeneficial handoffs rate.The simulation results show that the reduction of unnecessary handoffs proposed in our vertical handoff decision scheme reduces the handoff blocking probability, the packets loss rate and the handoff overhead     

A novel fast intra mode decision for versatile video coding

The latest video coding standard Versatile Video Coding (VVC) obtains superior coding efficiency compared to the High Efficiency Video Coding (HEVC), which is achieved by incorporating more effective and complex new coding tools. In this paper, we propose a novel fast intra mode decision algorithm for VVC, including following two strategies: (1) the correlation between the optimal modes of the adjacent blocks and the modes selected in the rough modes decision (RMD) process is analyzed and applied to reduce the modes in the candidate list; (2) modes in the candidate list are sorted in ascending order according to the modes’ cost calculated in the RMD process. An early termination method is proposed for terminating the optimal prediction mode decision process based on this new order early. These two strategies are incorporated into intra coding to reduce the coding complexity. Since these two strategies do not add any additional computational complexity, the proposed fast algorithm can achieve more complexity reduction. The experimental results show that the complexity reduction of the proposed algorithm is up to 44.74% compared to VVC reference software VTM2.0, and averagely 30.59% encoding time saving with 0.86% BDBR increase.     

Test sequencing and diagnosis in electronic system with decision table

In this paper, the decision table is used as a tool for representation of the test knowledge. With that table, the algorithm for building optimal decision trees, which embody the solution for the test sequencing and diagnosis problem, is analyzed. Some improvements in the algorithm are also proposed for better efficiency. Furthermore, in order to describe more complicated situation, the conditional probabilities are included in the decision table, called as conditional decision table. Different approaches for generating optimal conditional decision trees, based on the information theory and entropy, are proposed. Finally, the algorithm for building dynamic test and diagnostic procedures is also presented in this paper.     

一种改进的基于差别矩阵的属性约简算法

为解决决策表属性约简完备算法约简质量低的问题，在基于差别矩阵的属性约简完备算法的基础上，引入信息论中信息熵和互信息增益的定义，给出一种启发式属性约简完备方法，通过实例说明启发式信息可以提高完备算法的约简质量，比较不同启发信息对完备算法的约筒质量和约简效率。试验结果表明，采用基于信息论定义的两种启发信息的完备算法约筒效率基本一致，该算法较非启发式完备算法有更好的约简质量。     

一种基于遗传算法的SVM决策树多分类方法

在当前的机器学习领域,如何利用支持向量机（SVM）对多类目标进行分类,同时提高分类器的分类效率已经成为研究的热点之一,有效地解决此问题对于提高目标的识别概率具有较大意义。本文针对SVM多分类问题提出了一种基于遗传算法的SVM最优决策树生成算法。算法以随机生成的决策树构建的SVM分类器对同一测试样本的分类正确率作为遗传算法的适应度函数,通过遗传算法寻找到最优决策树,再以最优决策树构建SVM分类器,最终实现SVM的多分类。将该算法应用于低空飞行声目标识别问题,实验结果表明,新方法比传统的1-a-1、1-a-r、SVM-DL和GADT-SVM方法有更高的分类精度和更短的分类时间。      

A Knowledge Reduction Algorithm Based on Conditional Entropy

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Rate distortion optimization for H.264 interframe coding: a general framework and algorithms.

Rate distortion (RD) optimization for H.264 interframe coding with complete baseline decoding compatibility is investigated on a frame basis. Using soft decision quantization (SDQ) rather than the standard hard decision quantization, we first establish a general framework in which motion estimation, quantization, and entropy coding (in H.264) for the current frame can be jointly designed to minimize a true RD cost given previously coded reference frames. We then propose three RD optimization algorithms--a graph-based algorithm for near optimal SDQ in H.264 baseline encoding given motion estimation and quantization step sizes, an algorithm for near optimal residual coding in H.264 baseline encoding given motion estimation, and an iterative overall algorithm to optimize H.264 baseline encoding for each individual frame given previously coded reference frames-with them embedded in the indicated order. The graph-based algorithm for near optimal SDQ is the core; given motion estimation and quantization step sizes, it is guaranteed to perform optimal SDQ if the weak adjacent block dependency utilized in the context adaptive variable length coding of H.264 is ignored for optimization. The proposed algorithms have been implemented based on the reference encoder JM82 of H.264 with complete compatibility to the baseline profile. Experiments show that for a set of typical video testing sequences, the graph-based algorithm for near optimal SDQ, the algorithm for near optimal residual coding, and the overall algorithm achieve on average, 6%, 8%, and 12%, respectively, rate reduction at the same PSNR (ranging from 30 to 38 dB) when compared with the RD optimization method implemented in the H.264 reference software.