A GA-Based Approach to ICA Feature Selection: An Efficient Method to Classify Microarray Datasets

2009 ◽  
pp. 432-441
Author(s):  
Kun-Hong Liu ◽  
Jun Zhang ◽  
Bo Li ◽  
Ji-Xiang Du
Keyword(s):  
Feature Selection ◽  
Efficient Method ◽  
Microarray Datasets

scholarly journals Multi-Agent based Intelligent Decision Support Systems for Cancer Classification

2021 ◽  
Author(s):  
Hanaa Salem ◽  
Gamal Attiya ◽  
Nawal El-Fishawy
Keyword(s):  
Feature Selection ◽  
Decision Support ◽  
Machine Learning Algorithms ◽  
Final Report ◽  
Cad System ◽  
Intelligent Decision ◽  
Multi Agent ◽  
Microarray Datasets ◽  
Intelligent Decision Support ◽  
Cancer Diseases

There is evidence that early detection of cancer diseases can improve the treatment and increase the survival rate of patients. This paper presents an efficient CAD system for cancer diseases diagnosis by gene expression profiles of DNA microarray datasets. The proposed CAD system combines Intelligent Decision Support System (IDSS) and Multi-Agent (MA) system. The IDSS represents the backbone of the entire CAD system. It consists of two main phases; feature selection/reduction phase and a classification phase. In the feature selection/reduction phase, eight diverse methods are developed. While, in the classification phase, three evolutionary machine learning algorithms are employed. On the other hand, the MA system manages the entire operation of the CAD system. It first initializes several IDSSs (exactly 24 IDSSs) with the aid of mobile agents and then directs the generated IDSSs to run concurrently on the input dataset. Finally, a master agent selects the best classification, as the final report, based on the best classification accuracy returned from the 24 IDSSs The proposed CAD system is implemented in JAVA, and evaluated by using three microarray datasets including; Leukemia, Colon tumor, and Lung cancer. The system is able to classify different types of cancer diseases accurately in a very short time. This is because the MA system invokes 24 different IDSS to classify the diseases concurrently in parallel processing manner before taking the decision of the best classification result.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2020 ◽  
pp. 707-725
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets

2018 ◽  
Vol 73 ◽  
pp. 171-178 ◽  
Author(s):  
Mohammad K. Ebrahimpour ◽  
Hossein Nezamabadi-pour ◽  
Mahdi Eftekhari
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Scale Feature ◽  
Microarray Datasets

An Efficient Method for Solder Joint Inspection Based on Statistical Learning

2011 ◽  
Vol 121-126 ◽  
pp. 4931-4935
Author(s):  
Yong Cong Kuang ◽  
Gao Fei Ouyang ◽  
Hong Wei Xie ◽  
Xian Min Zhang
Keyword(s):  
Feature Selection ◽  
Solder Joint ◽  
Statistical Learning ◽  
Efficient Method ◽  
Solder Joints ◽  
Sampling Algorithm ◽  
Inspection Method ◽  
Defect Type ◽  
Imbalance Problem ◽  
Solder Joint Inspection

To improve the performance of current solder joint inspection method, an efficient method based on statistical learning is proposed in this paper. In the method, the solder was divided into several sub-regions to determine the defect type. To resolve imbalance problem, an improved over-sampling algorithm was proposed in which the synthetics samples are generated between the boundary samples and their neighbors. AdaBoost was used for feature selection and classification for every sub-region. Experiments results showed that the defects of solder joints can be identified properly using the proposed algorithm.

Performance Analysis of Microarray Data Classification using Machine Learning Techniques

2015 ◽  
Vol 5 (2) ◽  
pp. 43-54
Author(s):  
Subhendu Kumar Pani ◽  
Bikram Kesari Ratha ◽  
Ajay Kumar Mishra
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Microarray Data ◽  
Predictive Accuracy ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Data Mining Technique ◽  
Single Experiment ◽  
Learning Techniques ◽  
Microarray Datasets

Microarray technology of DNA permits simultaneous monitoring and determining of thousands of gene expression activation levels in a single experiment. Data mining technique such as classification is extensively used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, the authors use Particle Swarm Organization (PSO) and Genetic Algorithm (GA) to find the performance of several popular classifiers on a set of microarray datasets. Experimental results conclude that feature selection affects the performance.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2016 ◽  
pp. 17-36
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

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