scholarly journals Hepatitis Prediction Model based on Data Mining Algorithm and Optimal Feature Selection to Improve Predictive Accuracy

2012 ◽  
Vol 51 (19) ◽  
pp. 13-16 ◽  
Author(s):  
Varun Kumar.M ◽  
Vijaya Sharathi.V ◽  
Gayathri Devi.B.R
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Data Mining Algorithm ◽  
Model Based ◽  
Mining Algorithm ◽  
Optimal Feature Selection ◽  
Optimal Feature

scholarly journals Bio inspired data mining for optimizing GPCR function identification

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Bat Algorithm ◽  
Functional Class ◽  
Data Mining Algorithm ◽  
Combinatorial Explosion ◽  
Surface Receptors ◽  
Mining Algorithm ◽  
Representation Method

GPCR are the largest family of cell surface receptors; many of them still remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein, the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy (FES), data mining algorithm (DMA)). The authors propose also to use the BAT algorithm for extracting the pertinent features and the Genetic Algorithm to choose the best couple. They compared the results they we obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

scholarly journals OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73271-73284 ◽  
Author(s):  
Erzhou Zhu ◽  
Yuyang Chen ◽  
Chengcheng Ye ◽  
Xuejun Li ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Detection Model ◽  
Model Based ◽  
Optimal Feature Selection ◽  
Optimal Feature

Algorithm applied: attracting MSEs to business associations

2020 ◽  
Vol 35 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Jaqueline de Moraes ◽  
Jones Luís Schaefer ◽  
Jacques Nelson Corleta Schreiber ◽  
Johanna Dreher Thomas ◽  
Elpidio Oscar Benitez Nara
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Management Tool ◽  
Data Mining Algorithm ◽  
Business Associations ◽  
Structured Model ◽  
Content Type ◽  
Model Based ◽  
Mining Algorithm

Purpose This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members. Design/methodology/approach As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation. Findings The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association. Research limitations/implications This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus. Practical implications The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA. Social implications Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs. Originality/value This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

scholarly journals Bio-Inspired Data Mining for Optimizing GPCR Function Identification

2021 ◽  
Vol 15 (4) ◽  
pp. 1-31
Author(s):  
Safia Bekhouche ◽  
Yamina Mohamed Ben Ali
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Bat Algorithm ◽  
Functional Class ◽  
Data Mining Algorithm ◽  
Combinatorial Explosion ◽  
Surface Receptors ◽  
Mining Algorithm ◽  
Representation Method

GPCR are the largest family of cell surface receptors; many of them still remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein, the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy (FES), data mining algorithm (DMA)). The authors propose also to use the BAT algorithm for extracting the pertinent features and the Genetic Algorithm to choose the best couple. They compared the results they we obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

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