On the Stability of Server Selection Algorithms Against Network Fluctuations

2005 ◽  
pp. 210-224
Author(s):  
Toshiyuki Miyachi ◽  
Kenjiro Cho ◽  
Yoichi Shinoda
Keyword(s):  
Server Selection ◽  
The Stability ◽  
Selection Algorithms

scholarly journals An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

2021 ◽  
Vol 11 (15) ◽  
pp. 6983
Author(s):  
Maritza Mera-Gaona ◽  
Diego M. López ◽  
Rubiel Vargas-Canas
Keyword(s):  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Eeg Signals ◽  
Specificity And Sensitivity ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Stability ◽  
Selection Algorithms ◽  
Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

scholarly journals Stable Bagging Feature Selection on Medical Data

2020 ◽  
Author(s):  
Salem Alelyani
Keyword(s):  
Feature Selection ◽  
Sample Size ◽  
Variance Reduction ◽  
Small Sample Size ◽  
Small Sample ◽  
Domain Experts ◽  
Complex Dimensions ◽  
The Stability ◽  
Stability And Accuracy ◽  
Selection Algorithms

Abstract In the medical field, distinguishing genes that are relevant to a specific disease, let's say colon cancer, is crucial to finding a cure and understanding its causes and subsequent complications. Usually, medical datasets are comprised of immensely complex dimensions with considerably small sample size. Thus, for domain experts, such as biologists, the task of identifying these genes have become a very challenging one, to say the least. Feature selection is a technique that aims to select these genes, or features in machine learning field with respect to the disease. However, learning from a medical dataset to identify relevant features suffers from the curse-of-dimensionality. Due to a large number of features with a small sample size, the selection usually returns a different subset each time a new sample is introduced into the dataset. This selection instability is intrinsically related to data variance. We assume that reducing data variance improves selection stability. In this paper, we propose an ensemble approach based on the bagging technique to improve feature selection stability in medical datasets via data variance reduction. We conducted an experiment using four microarray datasets each of which suffers from high dimensionality and relatively small sample size. On each dataset, we applied five well-known feature selection algorithms to select varying number of features. The results of the selection stability and accuracy show the improvement in terms of both the stability and the accuracy with the bagging technique.

Efficient Management of Fragmented Replica in Data Grids

2012 ◽  
Vol 4 (2) ◽  
pp. 63-70 ◽  
Author(s):  
Chao-Chin Wu ◽  
Lien-Fu Lai ◽  
Jia-Xian Lai
Keyword(s):  
Optimal Solution ◽  
Selection Algorithm ◽  
Data Grids ◽  
Server Selection ◽  
Efficient Management ◽  
Replica Selection ◽  
Retrieval Efficiency ◽  
Simulation Results ◽  
Replication Technique ◽  
Selection Algorithms

Rather than replicating a file completely, Chang et al. proposed a fragmented replication technique to cope with the problem that only partial content of the replica are required in a local application. Furthermore, they also proposed two server selection algorithms for replica retrieval. However, their algorithms do not always find an optimal solution. To address the problem, in this paper, the authors propose a replica selection algorithm to improve the fragmented replica retrieval efficiency in this paper. It is a heuristic considering not only the transmission time but also the number of available servers for each block. Simulation results show that the proposed algorithm can improve the retrieval efficiency up to 12%.

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