An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory (original) (raw)
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An incremental rough set approach for faster attribute reduction
International Journal of Information Technology, 2019
In the view of performance improvement in machine learning algorithms it is essential to feed them with minimal and most relevant features. Feature selection is one of the evident preprocessing step followed by most of the learning algorithms for choosing the relevant features towards reducing the dimensionality of the dataset as well as to improve the classification accuracy. Among various feature selection techniques, Rough Set Theory has its own major contributions in feature selection domain. However, the conventional Rough Set based Feature Selection (RSFS) procedure takes up to O(kn 2) time and space complexity, where k is the number of objects and n is the total number of attributes, which is further reduced to O(kn). In this paper, an incremental approach of RSFS is proposed to reduce the time complexity of the algorithm to O(k log n) time. Here the indiscernibility relation is estimated in an incremental fashion rather than calculating them in conventional way, hence the overall attribute reduction time is reduced significantly when compared to conventional roughest approach. The performance of the proposed Incremental Rough Set (InRS) approach is evaluated with QuickReduct feature selection algorithm. The simulation results indicate that the InRS approach is able to find the reduct with minimal time complexity than the existing rough set methods.
An Innovative Approach for Attribute Reduction in Rough Set Theory
Intelligent Information Management, 2014
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.
Optimistic Rough Sets Attribute Reduction using Dynamic Programming
ijcset.com
Nowadays, and with the current progress in technologies and business sales, databases with large amount of data exist especially in Retail Companies. The main objective of this study is to reduce the complexity of the classification problems while maintaining the prediction classification quality. We propose to apply the promising technique Rough set theory which is a new mathematical approach to data analysis based on classification of objects of interest into similarity classes, which are indiscernible with respect to some features. Since some features are of high interest, this leads to the fundamental concept of "Attribute Reduction". The goal of Rough set is to enumerate good attribute subsets that have high dependence, discriminating index and significance. The naïve way of is to generate all possible subsets of attribute but in high dimension cases, this approach is very inefficient while it will require 1 2 d iterations. Therefore, we propose the Dynamic programming technique in order to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. Implementation has been developed, applied, and tested over a 3 years historical business data in Retail Business (RB). Simulations and visual analysis are shown and discussed in order to validate the accuracy of the proposed tool.
REDUCT GENERATION FOR THE INCREMENTAL DATA USING ROUGH SET THEORY
In today’s changing world huge amount of data is generated and transferred frequently. Although the data is sometimes static but most commonly it is dynamic and transactional. New data that is being generated is getting constantly added to the old/existing data. To discover the knowledge from this incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time consuming. The paper proposes a dimension reduction algorithm that can be applied in dynamic environment for generation of reduced attribute set as dynamic reduct. The method analyzes the new dataset, when it becomes available, and modifies the reduct accordingly to fit the entire dataset. The concepts of discernibility relation, attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of dynamic reduct set, which not only reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed method has been applied on few benchmark dataset collected from the UCI repository and a dynamic reduct is computed. Experimental result shows the efficiency of the proposed method.
A Novel Algorithm For Attribute Reduction in Rough Sets Based on Relation-Matrix
This paper has established correlation between information systems and relation matrix, in which a heuristic information was constructed from the viewpoint of relation matrix, objectively depicting the degree of importance of attributes. On this basis, an efficient information system of attribute reduction algorithm (ARFA) was proposed. Compared with the existing reduction algorithms, it has greater flexibility that can remove unimportant step by step, avoiding repeatedly calculation to its importance, and improve the search efficiency. The example analysis and experimental results showed that the reduction algorithm is both feasible and effective.
Fuzzy-Rough set Approach to Attribute Reduction
2017
Attribute Reduction has a significant role in different branches of artificial intelligence like machine learning, pattern recognition, data mining from databases etc. This paper deals with reduction of unimportant attribute(s) for classification and decision making, using Fuzzy-Rough set. A survey of Fuzzy-Rough set based methods for attribute reduction is presented here.
A Study on Rough Set Theory Based Dynamic Reduct for Classification System Optimization
International Journal of Artificial Intelligence & Applications, 2014
In the present day huge amount of data is generated in every minute and transferred frequently. Although the data is sometimes static but most commonly it is dynamic and transactional. New data that is being generated is getting constantly added to the old/existing data. To discover the knowledge from this incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary. The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The paper proposes a dimension reduction algorithm that can be applied in dynamic environment for generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the reduct and build up the corresponding classification system. The method analyzes the new dataset, when it becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data set, interesting optimal classification rule sets are generated. The concepts of discernibility relation, attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of dynamic reduct set, and optimal classification rules are selected using PSO method, which not only reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed method has been applied on some benchmark dataset collected from the UCI repository and dynamic reduct is computed, and from the reduct optimal classification rules are also generated. Experimental result shows the efficiency of the proposed method.
International Journal of Reasoning-based Intelligent Systems, 2015
In rough set theory, attribute reduction is an important application. Many approaches to attribute reduction were developed using rough set theory. These approaches used various greedy heuristics to find a reduct approximation. In this paper, we propose forward tentative selection with backward propagation of selection decision (FTSBPSD) algorithm to find a reduct. The proposed algorithm is based on the principle of indiscernibility of rough set theory. It finds one of the prime implicants of the discernibility function as the reduct. The proposed algorithm works for various types of reducts, defined in the rough set theory. In this work, we have analysed the performance of distribution reduct, maximum distribution reduct, positive region reduct and possible reduct. The proposed algorithm was tested on various datasets found in University of California, machine learning repository. It has given good results for classification accuracy during tests performed on the datasets. Experimental results obtained by FTSBPSD algorithm have been found to give better classification accuracy when tested using C4.5 classifier in comparison to the results obtained by the Q-MDRA algorithm described in the literature.
RECORD-TO-RECORD TRAVEL ALGORITHM FOR ATTRIBUTE REDUCTION IN ROUGH SET THEORY
Attribute reduction is the process of selecting a minimal attribute subset from a problem domain while retaining a suitably high accuracy in representing the original attributes. In this work, we propose a new attribute reduction algorithm called record-to-record travel (RRT) algorithm and employ a rough set theory as a mathematical tool to evaluate the quality of the obtained solutions. RRT is an optimization algorithm that is inspired from simulated annealing, which depends on a single parameter called DEVIATION. Experimental results on 13 well known UCI datasets show that the proposed method, coded as RRTAR, is comparable with other rough set-based attribute reduction methods available in the literature.
An efficient classifier design integrating rough set and set oriented database operations
Applied Soft Computing, 2011
Feature subset selection and dimensionality reduction of data are fundamental and most explored area of research in machine learning and data mining domains. Rough set theory (RST) constitutes a sound basis for data mining, can be used at different phases of knowledge discovery process. In the paper, by integrating the concept of RST and relational algebra operations, a new attribute reduction algorithm has been presented to select the minimum set of attributes, called reducts, required for classification of data. Firstly, the conditional attributes are partitioned into different groups according to their score, calculated using projection () and division (÷) operations of relational algebra. The groups based on their scores are sorted in ascending order while the first group contains maximum information is uniquely used for generating the reducts. The non-reduct attributes are combined with the elements of the next group and the modified group is considered for computing the reducts. The process continues until all groups are exhausted and thus a final set of reducts is obtained. Then applying decision tree algorithm on each reduct, decision rule sets are generated, which are later pruned by removing the extraneous components. Finally, by involving the concept of probability theory and graph theory minimum number of rules is obtained used for building an efficient classifier.