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In our previous study, a grouping-geneticalgorithm- based (GGA-based) attribute clustering process has been proposed for grouping features. In this paper, we further improve its performance and propose a center-based GGA for attribute clustering (CGGA). A new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better...
This paper aims at presenting a âcomputational costâ optimization method in an Automatic Music Genre Classification system. In such systems, the training and validation database is often enormous. Consequently, a system based on a nearest neighbor classifier suffers from high computational cost during the classification process. In such cases, a training instance clustering (per...
When considering data sets characterized by a large number of instances, the computational time required to apply Genetic Algorithms for generating Fuzzy Rule-Based Classifiers increases considerably, mainly due to the fitness evaluation. Another important problem associated to these kinds of data sets is an undesired increase of the obtained model complexity.
Given that real-world classification tasks always have irrelevant or noisy features which degrade both prediction accuracy and computational efficiency, feature selection is an effective data reduction technique showing promising performance. This paper presents a cooperative coevolution framework to make the feature selection process embedded into the classification model construction within the...
Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL Optimer (Evolutionary Optimization). In our method we used both filter and wrapper based approach for gene selection. The original subsets are...
According to the requirement of building data centers in State Grid project planning, the process of data cleaning was divided into two sub-processes in the data extraction process, namely, the abnormal values were set to NULL after detecting the electric quantity data, then, those data were predicted based on other valid values. To further improve the quality of data, we proposed a method which based...
Genetic operations that consider effective building blocks are proposed for using genetic algorithms to solve Sudoku puzzles. A stronger local search function is also proposed. Evaluation of the proposed techniques using commercial Sudoku puzzle sets and three puzzles ranked as super difficult compared with previously reported examples show that the rate of optimum solutions can be greatly improved...
Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes...
In the past, we proposed a GA-based clustering method for attribute clustering and feature selection. The fitness of each individual was evaluated using both the average accuracy of attribute substitutions in clusters and the cluster balance. The evaluation was, however, quite time-consuming. In this paper, we modify the previous method for a better execution performance based on feature similarity...
This paper aims to challenge the problem of finding accurate and relevant rules for the task of classification. The scope is to improve the accuracy, or at least to provide a comparable accuracy measure, for classification algorithms implemented so far. Because the task of classification must be as accurate as possible, the paper proposes a method based on genetic algorithms to enhance the speed and...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy...
The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier...
Relevance vector machine (RVM) is a state-of-the-art technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper proposes a novel regression...
An hybrid genetic algorithm was proposed to perform the optimal parameters for image registration. The genetic algorithm (GA) was combined with the Downhill Simplex search, and the GA algorithm considered the adaptive probabilities of mutation which change with the fitness distance correlation (FDC), mutual information (MI) was used as metric in this paper. The results using the hybrid genetic algorithm...
In this paper, a clustering method of attributes based on genetic algorithms is proposed for feature selection. It combines both the average accuracy of attribute substitution in clusters and the cluster balance as the fitness function. Experimental comparison with the k-means clustering approach and with all combinations of attributes also shows the effectiveness of the proposed approach. Besides,...
Shortest path routing is the type of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network...
Gene expression data usually contains a large number of genes (several thousand or more) but a small number of samples (usually <100). Among all the genes, many are irrelevant, insignificant or redundant to the discriminant problem under investigation. Hence the identification of informative genes, which have the greatest power for classification, is of fundamental and practical importance to the...
In this paper, the classification rule-mining problem is considered as a multi-objective problem rather than a uni-objective one. Metrics like predictive accuracy and comprehensibility, used for evaluating a rule can be thought of as different criteria of this problem. Predictive accuracy measures the accuracy of the rules extracted from the dataset where as, comprehensibility is measured by the number...
This paper proposes a hybrid genetic rule learning algorithm which incorporating feature selection technique. The chromosome of rule individual composed of two vectors: a rule condition vector representing the conjunction of rule conditions and a feature selection vector representing the selected features. In order to improve the performance of the algorithm, a local search method embedded in the...
Automatic categorization of documents into pre-defined taxonomies is a crucial step in data mining and knowledge discovery. Standard machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting...
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