RECORD-TO-RECORD TRAVEL ALGORITHM FOR ATTRIBUTE REDUCTION IN ROUGH SET THEORY (original) (raw)
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A fuzzy record-to-record travel algorithm for solving rough set attribute reduction
Attribute reduction can be defined as the process of determining a minimal subset of attributes from an original set of attributes. This paper proposes a new attribute reduction method that is based on a record-to-record travel algorithm for solving rough set attribute reduction problems. This algorithm has a solitary parameter called the DEVIATION, which plays a pivotal role in controlling the acceptance of the worse solutions, after it becomes pre-tuned. In this paper, we focus on a fuzzy-based record-to-record travel algorithm for attribute reduction (FuzzyRRTAR). This algorithm employs an intelligent fuzzy logic controller mechanism to control the value of DEVIATION, which is dynamically changed throughout the search process. The proposed method was tested on standard benchmark data sets. The results show that FuzzyRRTAR is efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.
Investigating Memetic Algorithm in Solving Rough Set Attribute Reduction
Attribute reduction is the problem of selecting a minimal subset from the original set of attributes. Rough set theory has been used for attribute reduction with much success. Since it is well known that finding a minimal subset is a NP-hard problem, therefore, it is necessary to develop efficient algorithms to solve this problem. In this work we propose a memetic algorithm based approach inside the rough set theory which is a hybridisation of genetic algorithm and simulated annealing. The proposed method has been tested on UCI datasets. Experimental results demonstrate the effectiveness of this memetic approach when compared with previous available methods. Possible extensions upon this simple approach are also discussed.
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.
—One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.
A novel rough set attribute reduction based on ant colony optimisation
International Journal of Intelligent Systems Technologies and Applications, 2015
Rough Sets (RS), the most promising and proven approach for data reduction, has the ability to retain the essence of the data and it does not expect any domain inputs from an expert. RS based reduction however can attain only local minima, so to elaborate the search space it is wise to employ well known and proven artificial intelligence techniques like the ant colony optimisation (ACO). In this work a novel rough set attribute reduction based on ACO, called as NRSACO is proposed, which can identify global optimal attribute set with the help of rough set based mutual information as a heuristic aid for the ants. Few improvements were suggested through which minimum reducts were attained faster and with fewer ants and iterations. Experiments were conducted on 22 UCI datasets, and the results shows that our approach has outperformed in convergence time with comparable or improved classification accuracies.
Fuzzy Population-Based Meta-Heuristic Approaches For Attribute Reduction In Rough Set Theory
2015
One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.
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.
Hybrid of genetic algorithm and great deluge algorithm for rough set attribute reduction
online.journals.tubitak.gov.tr
The attribute reduction problem is the process of reducing unimportant attributes from a decision system to decrease the difficulty of data mining or knowledge discovery tasks. Many algorithms have been used to optimize this problem in rough set theory. The genetic algorithm (GA) is one of the algorithms that has already been applied to optimize this problem. This paper proposes 2 kinds of memetic algorithms, which are a hybridization of the GA, with 2 versions (linear and nonlinear) of the great deluge (GD) algorithm. The purpose of this hybridization is to investigate the ability of this local search algorithm to improve the performance of the GA. In both of the methods, the local search (the GD algorithm) is employed to each generation of the GA. The only difference of these methods is the rate of increase in the 'level' in the GD algorithm. The level is increased by a fixed value in the linear GD algorithm, while the nonlinear GD algorithm uses the quality of the current solution to calculate the increase rate of the level in each iteration. The 13 datasets taken from the University of California -Irvine machine learning repository are used to test the methods and compare the results with the on-hand results in the literature, especially with the original GA. The classification accuracies of each dataset using the obtained reducts are examined and compared with other approaches using ROSETTA software. The promising results show the potential of the algorithm to solve the attribute reduction problem.
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.