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代价敏感属性约简的自适应分治算法
Adaptive divide and conquer algorithm for cost-sensitive attribute reduction
 [PDF]

黄伟婷,赵红,祝峰
HUANG Wei-ting
, ZHAO Hong, ZHU William

- , 2016, DOI: 10.6040/j.issn.1671-9352.0.2015.435
Abstract: 摘要: 代价敏感属性约简问题作为经典属性约简问题的自然扩展,将代价引入数据,使得属性约简问题更加具有现实意义。文章基于分治思想,先按列将数据集拆分为若干个互不相交的子数据集,然后对各子数据集进行约简,并把约简后的子数据集多路合并。依次继续执行约简和合并操作,最终得到最小测试代价约简。每个子数据集的大小及子数据集的总个数自适应于各个数据集的规模而非固定不变。为验证算法的有效性,选择四个UCI标准数据集进行实验,并与其他算法进行结果对比。实验结果表明,该算法能在较短时间内获得可接受的结果,更适应实际问题的需要。
Abstract: Cost-sensitive attribute reduction problem is the natural extension of classical attribute reduction, and it is more practical than the classical one by introducing cost. Based on divide and conquer thought, this paper proposes a new algorithm to deal with cost-sensitive attribute reduction. Firstly, the dataset is splitted into disjoint sub-datasets according to the number of the column. Then some sub-datasets are merged after backtracking reduction on each sub-dataset. Finally, it continues reducting and merging, and gets minimal test cost reduction. The size of the sub-datasets and the number of the sub-datasets are adaptive to the scale of the dataset rather than fixed. This algorithm is tested on four UCI datasets to verify its effectiveness. Compared with other algorithms, the experimental results show that the proposed algorithm can provide the efficient solution in a relatively short time
Quantization of Rough Set Based Attribute Reduction  [PDF]
Bing Li, Peng Tang, Tommy W. S. Chow
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.512B023
Abstract: We demonstrate rough set based attribute reduction is a sub-problem of propositional satisfiability problem. Since satisfiability problem is classical and sophisticated, it is a smart idea to find solutions of attribute reduction by methods of satisfiability. By extension rule, a method of satisfiability, the distribution of solutions with different numbers of attributes is obtained without finding all attribute reduction. The relation between attribute reduction and missing is also analyzed from computational cost and amount of solutions.
Analysis on attribute reduction strategies of rough set
Analysis on Attribute Reduction Strategies of Rough Set

Wang Jue,and Miao Duoqian,
Wang Jue
,Miao Duoqian

计算机科学技术学报 , 1998,
Abstract: Several strategies for the minimal attribute reduction with polynomial time complexity (O(nk)) have been developed in rough set theory. Are they complete? While investigating the attribute reduction strategy based on the discernibility matrix (DM),a counterexample is constructed theoretically, which demonstrates that these strategies are all incomplete with respect to the minimal reduction.
An Innovative Approach for Attribute Reduction in Rough Set Theory  [PDF]
Alex Sandro Aguiar Pessoa, Stephan Stephany
Intelligent Information Management (IIM) , 2014, DOI: 10.4236/iim.2014.65022
Abstract:

The Rough Sets Theory is used in data mining with emphasis on the treatment of uncertain or vague information. In the case of classification, this theory implicitly calculates reducts of the full set of attributes, eliminating those that are redundant or meaningless. Such reducts may even serve as input to other classifiers other than Rough Sets. The typical high dimensionality of current databases precludes the use of greedy methods to find optimal or suboptimal reducts in the search space and requires the use of stochastic methods. In this context, the calculation of reducts is typically performed by a genetic algorithm, but other metaheuristics have been proposed with better performance. This work proposes the innovative use of two known metaheuristics for this calculation, the Variable Neighborhood Search, the Variable Neighborhood Descent, besides a third heuristic called Decrescent Cardinality Search. The last one is a new heuristic specifically proposed for reduct calculation. Considering some databases commonly found in the literature of the area, the reducts that have been obtained present lower cardinality, i.e., a lower number of attributes.

DIMENSIONALITY REDUCTION – A ROUGH SET APPROACH
SABU M.K and RAJU G
International Journal of Machine Intelligence , 2011,
Abstract: In this paper we propose a novel approach of feature ranking for feature selection. This method is particularly useful for applications handling high dimensional datasets such as machine learning, pattern recognition and signal processing. This process is also applicable to small and medium sized datasets to identify significant features or attributes for a particular domain using the information contained in the dataset alone and hence the method preserves the meaning of the existing features. With the help of the proposed method, redundant attributes can be removed efficiently without sacrificing the classification performance. In this approach, after eliminating the outlier data elements from the dataset, features are ranked to identify the predominant features of the dataset. The discernibility matrix in RST is used as a tool to discover the data dependencies existing between various features and features are ranked based on these data dependencies. A method using Centre of Gravity (CoG) line is suggested to determine this discrimination frequency within a reduced computational effort. To evaluate the performance of the algorithm, we applied the proposed algorithm on a test dataset consisting of 3000 offline handwritten samples of 10 Tamil characters. The outcome of the experiment shows that the new method is efficient and effective for dimensionality reduction.
Rough Set Approach to Approximation Reduction in Ordered Decision Table with Fuzzy Decision  [PDF]
Xiaoyan Zhang,Shihu Liu,Weihua Xu
Mathematical Problems in Engineering , 2011, DOI: 10.1155/2011/268929
Abstract: In practice, some of information systems are based on dominance relations, and values of decision attribute are fuzzy. So, it is meaningful to study attribute reductions in ordered decision tables with fuzzy decision. In this paper, upper and lower approximation reductions are proposed in this kind of complicated decision table, respectively. Some important properties are discussed. The judgement theorems and discernibility matrices associated with two reductions are obtained from which the theory of attribute reductions is provided in ordered decision tables with fuzzy decision. Moreover, rough set approach to upper and lower approximation reductions is presented in ordered decision tables with fuzzy decision as well. An example illustrates the validity of the approach, and results show that it is an efficient tool for knowledge discovery in ordered decision tables with fuzzy decision. 1. Introduction Rough set theory, which was first proposed by Pawlak in the early 1980s [1], can describe knowledge via set-theoretic analysis based on equivalence classification for the universe of discourse. It provides a theoretical foundation for inference reasoning about data analysis and has extensive applications in areas of artificial intelligence and knowledge acquisition. A primary use of rough set theory is to reduce the number of attributes in databases thereby improving the performance of applications in a number of aspects including speed, storage, and accuracy. For a data set with discrete attribute values, this can be done by reducing the number of redundant attributes and find a subset of the original attributes that are the most informative. As is well known, an information system may usually has more than one reduct. It means that the set of rules derived from knowledge reduction is not unique. In practice, it is always hoped to obtain the set of the most concise rules. Therefore, people have been attempting to find the minimal reduct of information systems, which means that the number of attributes contained in the reduction is minimal. Unfortunately, it has been proven that finding the minimal reduct of an information system is an NP-hard problem. Recently, some new theories and reduction methods have been developed. Many types of knowledge reduction have been proposed in the area of rough sets [2–8]. Possible rules and reducts have been proposed as a way to deal with inconsistence in an inconsistent decision table [9]. Approximation rules [10] are also used as an alternative to possible rules. On the other hand, generalized decision rules and
A Divide-and-Conquer Strategy for Parsing  [PDF]
Peh Li Shiuan,Christopher Ting Hian Ann
Computer Science , 1996,
Abstract: In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separate parsing of the constituent sub-sentences within a complex sentence. To achieve that, the divide-and-conquer strategy first disambiguates the roles of the link words in the sentence and segments the sentence based on these roles. The separate parse trees of the segmented sub-sentences and the noun phrases within them are then synthesized to form the final parse. To evaluate the effects of this strategy on parsing, we compare the original performance of a dependency parser with the performance when it is enhanced with the divide-and-conquer strategy. When tested on 600 sentences of the IPSM'95 data sets, the enhanced parser saw a considerable error reduction of 21.2% in its accuracy.
Rough Set Reduction Using Method of Closed Operator
粗糙集约简的闭算子方法

WEI Ling,ZHANG Wen-Xiu,
魏玲
,张文修

计算机科学 , 2007,
Abstract: Attribute reduction is one of the most important problems in knowledge discovery in information system. The general method to study attribute reduction in information system is rough set theory, whose theoretical basis is the equivalence relations on attr
Attribute reduction algorithm for huge data based on rough set theory
一种基于Rough集的海量数据属性约简方法

HU Feng,ZHANG Jie,LIU Jing,XIAO Da-wei,
胡峰
,张杰,刘静,肖大伟

重庆邮电大学学报(自然科学版) , 2009,
Abstract: The attribute reduction of huge data is a difficult problem in the research of data mining. At present, many attribute reduction algorithms lack consideration on space complexity, which makes them cannot adapt to the reduction of large data set. In this paper, an attribute reduction algorithm of ordered attributes was proposed based on the divide and conquer, and this algorithm can be used to deal with huge data reduction. Simulation results show the efficiency of the algorithm.
Question Classification based on Rough Set Attributes and Value Reduction  [PDF]
Li Peng,Zhang Kai-Hui
Information Technology Journal , 2011,
Abstract: This study presents a method on automatic question classification through attribute and value reduction based on rough set theory. The core of the method is adopting statistical machine learning, with the assistance of a fair number of training corpus, attempts to automatically obtain useful and concise classification rules. Attributes reduction algorithm can omit the attributes which are unnecessary to decision classification in the decision table so as to simplify the decision table and increase the adaptability of decision process. The value reduction algorithm based on attributes significance can further eliminate the unnecessary information in the decision table. Comparing with the alternative means under the same data set and classification architecture, the experiment result is that the accuracy of the rough classification in this study is up to 86.20%, fine classification reaches 78.8%. It means that the method of this study is efficient.
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