Ant Colony reduction with modified rules generation for rough classification model.
ABSTRACT In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct. The performance of the proposed classifier is compared with the DRGF based classifier using genetic reduction. The experimental results show that the ACO-Rough performs better with higher classification accuracy and fewer number of rules.
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ABSTRACT: We propose a new feature selection strategy based on rough sets and particle swarm optimization (PSO). Rough sets have been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.Pattern Recognition Letters. 01/2007;
Article: Rough sets theory[Show abstract] [Hide abstract]
ABSTRACT: The basic concepts of the rough set theory are introduced and adequately illustrated. An example of the rough set theory application to the QSAR classification problem is presented. Numerous earlier applications of rough set theory to the various scientific domains suggest that it also can be a useful tool for the analysis of inexact, uncertain, or vague chemical data.Chemometrics and Intelligent Laboratory Systems - CHEMOMETR INTELL LAB SYST. 01/1999; 47(1):1-16.
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ABSTRACT: Extensive amounts of knowledge and data stored in medical databases require the de- velopment of specialized tools for storing, accessi ng, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases. In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.Informatica, Lith. Acad. Sci. 01/2004; 15:23-38.