Knowledge Reduction Algorithm Research Articles

Index Predigesting Method of ELINT System Based on MIBARK Algorithm

The effectiveness evaluation of the Electronic Information (ELINT) system, which plays an important role in guiding the theoretical research, equipment development, and practical application, is the key technology of the ELINT system. In practical applications, the effectiveness evaluation of ELINT system is mainly aimed at evaluation methods. However, the establishment of evaluation criteria and the construction of the index system are still two main challenges in this field, especially the optimization of the evaluation index system. In this paper, we aim at establishing the ELINT system index evaluation criteria and optimizing the ELINT system evaluation index system. Based on the principle structure of the ELINT system, we directly construct the original efficiency index system, establish specific evaluation criteria for each index, and quantify the indexes using the criteria. To optimize the proposed index system, we introduce the idea of rough set reduction and develop an index system reduction model based on the mutual information heuristic knowledge reduction (MIBARK) algorithm. Simulation analysis shows that the proposed evaluation criteria quantify each index scientifically, and the established indicator reduction model can eliminate the redundancy of the ELINT efficiency indicator system, making the indicator system more streamlined and reasonable.

A knowledge reduction approach for linguistic concept formal context

Formal concept analysis (FCA) has been widely studied as an important tool for data processing and knowledge discovery. The present work focuses on FCA under uncertainty while the attributes are described with linguistic terms or attribute description are incomplete. Accordingly, a linguistic concept formal context is introduced first. With an attempt of knowledge reduction, the multi-granularity similarity relationship between linguistic concepts is defined on the basis of granular computing which further divides the linguistic concept set into three parts under λ-granularity (i.e., core linguistic concept, unnecessary linguistic concept, and relative necessary linguistic concept). A multi-granularity linguistic reduction algorithm of incomplete linguistic concept formal context is then introduced. To handle the incompleteness, a new algorithm to complete the incomplete linguistic concept formal context based on the closeness degree between fuzzy objects is proposed. Finally, based on the Boolean matrix and Boolean factor analysis method, the linguistic concept knowledge reduction algorithm to extract the core linguistic concept and reduce the scale of linguistic concept lattice is proposed to handle the complexity, which is achieved by computing the similarity of linguistic concept knowledge in order to handle different types of linguistic information and concept knowledge. The effectiveness and practicability of the proposed model are illustrated by examples.

A hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm with robust ensemble Pareto equilibrium

With the rapid development of electronic, engineering, information and network technologies, large-scale data mining and knowledge discovery have become a new challenge. In this paper, a hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm (HCMRKR) with robust ensemble Pareto dominance is proposed for big data analysis. Firstly, a novel hierarchical co-evolutionary MapReduce framework with niche neighborhood radius is designed to divide the entire population into N subpopulations. The layered niche neighborhood radius is constructed so that subpopulations can be self-adaptively decomposed into the attribute approximate space with uncovering the underlying interaction structure of decision attributes. The reduction performances of elitist leaders are surely guaranteed to be the same with those using the whole independent dataset. Secondly, a robust ensemble strategy of reduction Pareto equilibrium is adopted to help elitist leaders from the Pareto front region to conduct the ensemble game of reduction subsets in different niche conic subspaces. Thus, most reduction solutions will be enhanced to be more robust within the stable elitists region. Thirdly, various elitist leaders will use the parallel MapReduce mechanism to determine the best strategy to extract their knowledge reduction set, and the adaptive reduction evolution with incremental learning can be implemented well. Experimental results illustrate that the proposed HCMRKR algorithm can effectively and efficiently handle the knowledge reduction problem in the big data sets as well as keeping more robust and stable performances, compared with existing state-of-the-art algorithms. Furthermore, the validation performed on two kinds of neonatal brain MRIs further demonstrates the HCMRKR's promising advantage for the real-world applications.

Personalized Product Design Knowledge Acquisition Based on Knowledge Reduction and Knowledge Mining

This paper makes the historical data which customers bought the products as the foundation, and suggests a method of personalized product design knowledge acquisition based on knowledge reduction and knowledge mining. Firstly, the core matrices of each specific customer group were taken as the decision variables of the decision table based on the key customer segmentation. Secondly, the knowledge reduction algorithm of Skowron discernibility matrix was use to acquire the product design information by reducing the decision table, and deleting the redundant and even product matrices that are not necessary. Thirdly, the product design information was taken as the initial data of knowledge mining, and the product design knowledge of specific customer group was obtained by a classification consistency algorithm. Finally, the stroller experimental design was taken as a case study by online interaction based on Web technology, and the conclusion of the study showed that the method was feasible.

The Application of Incomplete Information System of Rough Set Theory in Spam Filtering

The spread of spam brings a great deal of disturbance to e-mail users. In order to improve the efficiency of spam filtering and solve the problem that some attributes extracted from the e-mail header are null, we first applied the knowledge reduction algorithm and the sample recognition algorithm based on incomplete information system of Rough Set theory to spam filtering on e-mail header. In this paper, we proposed an improved non-symmetric similarity relation and defined the attribute complete importance for attribute reduction. Experimental results showed that the algorithm got higher recall and precision, lower fake recognition than other algorithms.

The Large-scale Dynamic Data Rapid Reduction Algorithm Based on Map-Reduce

With the advent of the era of “Big Data”, the application of the large-scale data is becoming popular. Efficiently using and analyzing the data has become an important problem. Traditional knowledge reduction algorithm read small data samples once into the computer main memory for reduction, but it is not suitable for large-scale data. This paper takes the large-scale sensor monitoring dynamic data as the research object and puts forward an incremental reduction algorithm based on Map-Reduce. Using Hash fast partitioning strategy this algorithm divides the initial data set into multiple subdatasets to compute which has greatly reduced the calculation time and space complexity of each node. Finally，through some experiments on the data sets in UCI machine learning repository based on Hadoop platform，the algorithm is proved more efficient and suitable for large-scale dynamic data. Compared to the traditional algorithm, the highest speedup of the parallel algorithm can be increased up to 1.55 times.

Fuzzy belief measure in random fuzzy information systems and its application to knowledge reduction

In a random fuzzy information system, by introducing a fuzzy t-similarity relation on the objects set for a subset of attributes set, the approximate representations of knowledge are established. By discussing fuzzy belief measures and fuzzy plausibility measures defined by the lower approximation and the upper approximation in a random fuzzy approximation space, some equivalent conditions of knowledge reduction in a random fuzzy information system are proved. Similarly as in an information system, the fuzzy-set-valued attribute discernibility matrixes in a random fuzzy information system are constructed. Knowledge reduction is defined from the view of fuzzy belief measures and fuzzy plausibility measures and a heuristic knowledge reduction algorithm is proposed, and the time complexity of this algorithm is O(|U|2|A|). A running example illustrates the potential application of algorithm, and the experimental results on the data sets with numerical attributes show that the proposed method is effective.

Knowledge Reduction Algorithms in Cloud Computing

Knowledge reduction is one of the important research issues in rough set theory.Classical knowledge reduction algorithms assume all the datasets can be loaded into the main memory,which are infeasible for large-scale datasets.Massive data with high dimensions makes attribute reduction a challenging task.To this end,the concepts and properties of discernibility and indiscernibility object pairs are given in terms of the discernibility and indiscernibility of the attribute(s).The relationship between discernibility matrix and them is illustrated in detail.Then,an algorithm of computing equivalence classes is designed for large-scale data in data parallel,and the corresponding knowledge reduction algorithms are proposed in cloud computing.Finally,three parallelism strategies are implemented and discussed.The experimental results demonstrate that knowledge reduction algorithms in cloud computing can scale well and efficiently process massive datasets on commodity computers.

Approaches to Knowledge Reduction of Decision Systems based on Conditional Rough Entropy

Knowledge reduction in rough set theory is an important feature selection method. Since it is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, to address this issue, by introducing rough entropy in information systems, the novel measures of conditional rough entropy with distinguishing consistent objects form inconsistent objects are presented for both consistent and inconsistent decision systems. Thus, many important propositions, properties, and conclusions for reduct are drawn, and by using decomposition, radix sorting, hash, and input sequence techniques, we construct a forward greedy algorithm for knowledge reduction. Finally, through analyzing the given example, compared with some standard UCI datasets and other knowledge reduction algorithms, the proposed technique is effective and suitable for both consistent and inconsistent decision systems. Thus, it establishes the theoretical basis for seeking efficient algorithm of knowledge acquisition in decision systems.

A Quick Algorithm for Attribute Reduction Based on Divide and Conquer Method

Knowledge reduction is one of the most important contributions of rough set theory. High efficient algorithm is necessary for attribute reduction. In this paper, combining given attribute order and quick sort method, a heuristic algorithm for attribute reduction is developed. In this method, the objects in universe are divided into many small parts on the attributes by divide and conquer method, and different granules in different granular levels are obtained. The small ones can be processed quickly, so a fast heuristic algorithm for attribute reduction is proposed. Simulation experiment results illustrate the high efficiency of the improved knowledge reduction algorithm.

Variable Rough Set Model and Its Knowledge Reduction for Incomplete and Fuzzy Decision Information Systems

The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is the generalization of the variable precision rough set model for incomplete information system and that of rough set model for incomplete and fuzzy decision information system. The knowledge reduction and heuristic algorithm, built on the method and theory of precision reduction, are proposed. Keywords—Rough set, Incomplete and fuzzy decision information system, Limited valued tolerance relation, Knowledge reduction, Variable rough set model