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One of the interesting and important subjects among researchers in the field of medical and computer science is diagnosing illness by considering the features that have the most impact on recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM). Indeed, data mining methods use different ways such as classification and clustering to classify diseases and their symptoms...
Web recommendation systems are helpful in overcoming the excess information on web by retrieving the information required by the user with respect to user's or similar users' preferences and interests. In order to make web recommendation system work, web users have to be clustered based on their common interest. The web user clusters are used to obtain the knowledge about the web pages accessed. This...
We take inspirations from nature very often in solving many complex scientific and day to day problems. Nature inspired computing is a branch of computer engineering deals with the development of algorithms simulating behaviors of natural species for solving complex problems not easily solvable by available computational models. Based on biological systems, various algorithms have been presented in...
the division of the test paper can reflect the quality of examination paper, but it is difficult to find some decisive courses in dozens of courses. In order to find out the curriculum that decides the role of different levels of students, the concept of course discrimination is proposed, which focuses on the value of course discrimination, the classification method and the proportion of special courses...
Hyperspectral remote sensing is becoming an active research field in the last decades thanks to the availability of efficient machine learning algorithms and also to the ever-increasing computation power. However, there exist application domains (e.g., embedded applications) in which the deployment of this kind of systems becomes unfeasible due to the high requirements related to the size, power consumption...
In the highway traffic abnormal state detection, Support Vector Machine (SVM) algorithm is widely researched in recent years, but it still has some limitations. Aiming at the problem of improper selection of feature vector, the space and time characteristics of highway traffic abnormal state data is summarized, and the feature vectors of SVM are selected by Principal Component Analysis (PCA) properly...
In order to improve the accuracy of support vector machine (SVM) classification of wetland remote sensing images, the selection of kernel function parameters in support vector machines becomes an effective approach. In this paper, Particle Swarm Optimization and Genetic Algorithms (PSO-GA) co-evolutionary algorithm are used to optimize the SVM parameters. Because of the complementarity of evolutionary...
In this paper we explore the problem of autotuning the choice of algorithm. For a given task, there may be multiple algorithms available, each of which may contain its own set of tunable parameters and may provide optimal performance under different sets of inputs. Algorithmic choice is a type of tuning parameter which has not been well studied in the history of autotuning. To close this gap, we examine...
Nature inspired algorithms are gaining popularity for optimizing complex problems. These algorithms have been classified into 2 general categories, namely Evolutionary and Swarm Intelligence, which have further been divided into a couple of algorithms. This paper presents a comparative study between Bat Algorithm, Genetic algorithm, Artificial Bee Colony Algorithm and Ant Colony Optimization Algorithm...
The brain-computer interface (BCI), identify brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper identification of electroencephalogram (EEG) based BCI for motor imagery (MI) task is done through asynchronous approach. Transferring the brain computer interface (BCI) from laboratory state to real time application desires BCI...
In text classification, feature selection is essential to improve the classification effectiveness. This paper provides an empirical study of a feature selection method based on genetic algorithms for different text representation methods. This feature selection algorithm can accomplish two goals: in one hand is the search of a feature subset such that the performance of classifier is best; in other...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
The goal of the paper is the analysis of the fuzzy classifiers effectiveness, which are built by different algorithms of feature selection according to wrapper algorithms. The search of the informative features is provided on basis of the greedy algorithm (GrA), the discrete genetic algorithm (GA), the discrete mine blast algorithm (MBAd).
Pupillary changes can be used to detect human condition. Several studies on pupillary changes were carried out in order to study human emotions such as annoyance, stress, and reactions when viewing pleasant or unpleasant images, or seeing exciting products. A person's emotions can also be detected from pupil diameter when solving math problems using samples or independently. In medical field, pupil...
To increase the efficiency of conventional Segmentation Based Fractal Texture Analysis (SFTA), we propose a new approach on SFTA algorithm. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with the optimization technique for classification based on grey level range to get more accurate output. Experimental results...
In previous studies, many scholars use the global search ability of Genetic Algorithm (GA) to optimize the Fuzzy C-means (FCM) clustering algorithm to obtain better clustering center. But the prematurity problem of GA itself has bad effects on the whole clustering. Therefore, in order to optimize the traditional GA-FCM algorithm's clustering effect, in this paper, we introduce the Opposition-based...
In recent years, with the text, images, audio, video and other data doubled because of the rapid development of information technology, we have entered the era of big data. How to effectively analyze and use the data has been a hot research direction. So the research of distributed database came into being to adapt to this research requirement. Sometimes the traditional genetic algorithm can't generate...
Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers...
Aerobics is a new sport in our country. Because of different ethnic groups, there are differentiated competition. How to carry on the quantitative research to our country youth special skill is a major task to improve our athletes comprehensive quality and competitive ability of athletes. In this paper, based on the modern intelligent algorithm, the high precision classification performance of support...
The challenge to choose the best algorithm and its best parameters for a given problem is known as Combined Algorithm Selection and Hyperparameter Optimization Problem. Among all the classification algorithms available are those based on human comprehensible representations, such as decision trees and classification rule induction. These algorithms are usually chosen by the clarity of the results...
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