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A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
The paper adopts the fuzzy c-means text mining method in lots of text mining methods. But aim at the defect that the initial value of the fuzzy c-means is more sensitivity and poor stability, an improved GAFCM text mining method has been put forward. GAFCM uses global search features of genetic algorithms to improve the fuzzy c-means. Finally, it has proved that the improved text mining method has...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of...
Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been...
This work explores the use of clustering objectives in a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, under the application of flow based encrypted traffic identification. We first explore whether it is possible to achieve the performance of a gold standard model (i.e., classification objectives), using a MOGA based on clustering objectives....
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...
The paper suggests a customized elite based genetic programming technique for the identification of complex nonlinear systems. The models generated by the proposed method are nonlinear, linear in parameters, as the universal approximation capacities of such a mathematical formalism have been rigorously proven. To better exploit the models' parameter wise linearity, the authors propose a memetic approach...
As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading...
This paper proposes an improved genetic algorithm, it keeps the population diversity by similarity checks on the population before selection, and the algorithm solves the early-maturing problem of the population evolution, and proposes a formula for mutation probability related with similarity rate and iteration times. The algorithm not only maintains a good diversity of population, but also guarantees...
It has often been pointed out that each color has a particular impression and that there are certain similarities and individual differences in the relationships between a color and its impression. We now present a few examples of individual studies that have focused on color impressions. In this paper, in order to examine individual characteristics of color impression, we propose a method that combines...
Though the hybrid clustering algorithm (HCA) is very effective to cluster Web pages, it needs the auto k value calculation (AKVC) method to calculate the number of clusters in advance and its clustering result is affected by the number. A dynamic genetic algorithm(DGA) is designed in this paper by improving the AKVC method and the HCA's population, genetic operators and fitness function. The experiments...
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phase and parameters modified phase. Firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training...
Graph or network clustering is one of the fundamental multimodal combinatorial problems that have many applications in computer science. Many algorithms have been devised to obtain a reasonable approximate solution for the problem. Current approaches, however, suffer from the local optimum drawback and then have difficulty splitting two clusters with very confused structures. In this paper we propose...
Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual...
Overlap decomposition is one of the difficulties in spike sorting. A method based on genetic algorithm is proposed to deal with the overlapped signal from two single waves in this paper. Specifically, with the reliable signal wave templates given, the methods for overlap signals and for single wave signals are used simultaneously to process the unknown data from the signal acquisition equipment, and...
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,...
The shortcomings about present genetic algorithm applying to classification are analyzed. Using the method of minimum propagating tree can cluster complex shape and non-overlap sample candidate solutions into races. The algorithm regulates optimization with "race" method and controls individuals in a micro way with race crossover. We also mixed crossover operator based on the thought of...
Credit scoring has been regarded as a critical topic and studied extensively in the finance field. Many artificial intelligence techniques have been used to solve credit scoring. The paper is to build a classification model based on a decision tree by learning historical data. Clustering algorithm and genetic algorithm are combined to further improve the accuracy of this credit scoring model. The...
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