Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

2017 ◽  
pp. 41-70 ◽  
Author(s):  
Arunkumar Chinnaswamy ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Information Gain ◽  
Feature Subset ◽  
Classification Problems ◽  
Raw Data ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach ◽  
Gene Expression Levels

The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.

A Combination of Shuffled Frog-Leaping Algorithm and Genetic Algorithm for Gene Selection

2008 ◽  
Vol 12 (3) ◽  
pp. 218-226 ◽  
Author(s):  
Cheng-San Yang ◽  
◽  
Li-Yeh Chuang ◽  
Chao-Hsuan Ke ◽  
Cheng-Hong Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Expression Profiles ◽  
Classification Problems ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Microarray data referencing to gene expression profiles provides valuable answers to a variety of problems, and contributes to advances in clinical medicine. The application of microarray data to the classification of cancer types has recently assumed increasing importance. The classification of microarray data samples involves feature selection, whose goal is to identify subsets of differentially expressed gene potentially relevant for distinguishing sample classes and classifier design. We propose an efficient evolutionary approach for selecting gene subsets from gene expression data that effectively achieves higher accuracy for classification problems. Our proposal combines a shuffled frog-leaping algorithm (SFLA) and a genetic algorithm (GA), and chooses genes (features) related to classification. The K-nearest neighbor (KNN) with leave-one-out cross validation (LOOCV) is used to evaluate classification accuracy. We apply a novel hybrid approach based on SFLA-GA and KNN classification and compare 11 classification problems from the literature. Experimental results show that classification accuracy obtained using selected features was higher than the accuracy of datasets without feature selection.

scholarly journals Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

2019 ◽  
Vol 15 (08) ◽  
pp. 62 ◽  
Author(s):  
Mohammad Subhi Al-Batah ◽  
Belal Mohammad Zaqaibeh ◽  
Saleh Ali Alomari ◽  
Mowafaq Salem Alzboon
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Gene Selection ◽  
Selection Process ◽  
Decision Table ◽  
Gene Microarray ◽  
Classification Problems ◽  
Number Of Genes ◽  
Correlation Based Feature Selection ◽  
Microarray Classification

Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes.

scholarly journals A Constrained Feature Selection Approach Based on Feature Clustering and Hypothesis Margin Maximization

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Samah Hijazi ◽  
Vinh Truong Hoang
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Subset ◽  
Feature Clustering ◽  
Discriminative Feature ◽  
Selection Approach ◽  
Benchmark Datasets ◽  
Margin Maximization ◽  
Feature Selection Approach ◽  
The Right

In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are then hierarchically clustered identifying groups of the most similar ones. Finally, a semisupervised margin-based objective function is optimized to select the most data discriminative feature from within each cluster, hence maximizing relevance while minimizing redundancy among features. Eventually, we empirically validate our proposed approach on multiple well-known UCI benchmark datasets in terms of classification accuracy and representation entropy, where it proved to outperform four other semisupervised and unsupervised methods and competed with two widely used supervised ones.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 187
Author(s):  
Rattanawadee Panthong ◽  
Anongnart Srivihok
Keyword(s):  
Feature Selection ◽  
Liver Cancer ◽  
Predictive Model ◽  
Information Gain ◽  
Classification Performance ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Cancer Data

Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

2019 ◽  
Vol 18 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification Accuracy ◽  
B Cell Lymphoma ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

scholarly journals Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 6907-6911
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Water Buffalo ◽  
Information Gain ◽  
Sampling Technique ◽  
Multi Layer Perceptron ◽  
Analysis Model ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Correlation Based Feature Selection

This research aims to develop the analysis model for diseases in water buffalo towards the application of the feature selection technique along with the Multi-Layer Perceptron neural network. The data used for analysis was collected from books and documents related to diseases in water buffalo and the official website of the Department of Livestock Development. The data consists of the characteristics of six diseases in water buffalo, including Anthrax disease, Hemorrhagic Septicemia, Brucellosis, Foot and Mouth disease, Parasitic disease, and Mastitis. Since the amount of the collected data was limited, the Synthetic Minority Over-sampling Technique was also employed to adjust the imbalance dataset. Afterward, the adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, including Correlation-based Feature Selection and Information Gain. Subsequently, the selected features were then used for developing the analysis model for diseases in water buffalo towards the use of Multi-Layer Perceptron neural network. The evaluation results of the model’s effectiveness, given by the 10-fold cross-validation, showed that the analysis model for diseases in water buffalo developed by Correlation-based Feature Selection and Multi-Layer Perceptron neural network provided the highest level of effectiveness with the accuracy of 99.71%, the precision of 99.70%, and the recall of 99.72%. This implies that the analysis model is effectively applicable.

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