Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

Hamraz, Muhammad; Gul, Naz; Raza, Mushtaq; Khan, Dost Muhammad; Khalil, Umair; Zubair, Seema; Khan, Zardad

doi:10.7717/peerj-cs.562

Cited by 14 publications

(6 citation statements)

References 55 publications

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“…Performance of the proposed method could further be improved by using appropriate distance formula to determine the paths. Another possible way to improve performance of the method is to use the feature selection procedures as given in [59,60,61,62,63,64,65]. This could be used for selecting a set of features from the total feature space for model construction.…”

Section: Discussionmentioning

confidence: 99%

A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets

Ali¹,

Hamraz²,

Gul³

et al. 2022

Preprint

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kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting. The ordinary ensembles based on kNN find out the k nearest observations in a region (bounded by a sphere) based on a predefined value of k. This scenario, however, might not work in situations when the test observation follows the pattern of the closest data points with the same class that lie on a certain path not contained in the given sphere. This paper proposes a k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. This new ensemble method is applied on 17 benchmark datasets and compared with other classical methods, including kNN based models, in terms of classification accuracy, kappa and Brier score as performance metrics. Boxplots are also utilised to illustrate the difference in the results given by the proposed and other state-of-the-art methods. The proposed method outperformed the rest of the classical methods in the majority of cases. The paper gives a detailed simulation study for further assessment.

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Section: Discussionmentioning

confidence: 99%

A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets

Ali¹,

Hamraz²,

Gul³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The performance can further be improved by selecting a suitable distance formula. Feature selection method such as [66][67][68][69], could be used for further improvement in the performance of the proposed method.…”

Section: Discussionmentioning

confidence: 99%

A k-Nearest Neighbours Based Ensemble via Optimal Model Selection for Regression

et al. 2020

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Ensemble methods based on k-NN models minimise the effect of outliers in a training dataset by searching groups of the k closest data points to estimate the response of an unseen observation. However, traditional k-NN based ensemble methods use the arithmetic mean of the training points' responses for estimation which has several weaknesses. Traditional k-NN based models are also adversely affected by the presence of non-informative features in the data. This paper suggests a novel ensemble procedure consisting of a class of base k-NN models each constructed on a bootstrap sample drawn from the training dataset with a random subset of features. In the k nearest neighbours determined by each k-NN model, stepwise regression is fitted to predict the test point. The final estimate of the target observation is then obtained by averaging the estimates from all the models in the ensemble. The proposed method is compared with some other state-of-the-art procedures on 16 benchmark datasets in terms of coefficient of determination (R 2 ), Pearson's product-moment correlation coefficient (r), mean square predicted error (M SP E), root mean squared error (RM SE) and mean absolute error (M AE) as performance metrics. Furthermore, boxplots of the results are also constructed. The suggested ensemble procedure has outperformed the other procedures on almost all the datasets. The efficacy of the method has also been verified by assessing the proposed method in comparison with the other methods by adding non-informative features to the datasets considered. The results reveal that the proposed method is more robust to the issue of non-informative features in the data as compared to the rest of the methods.

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“…Muhammad Ali et al [ 50 ] analyzed the ANN and SVM classifiers on the stock dataset and proved that the ANN performs better. In [ 51 ], the RPOS feature selection method is proposed and its performance on the RF, SVM, and KNN is analyzed. Ishfaq Ali et al [ 52 ] in their research used a data-driven approach to decide on the number of clusters, K in the K-means clustering algorithm, and in [ 53 ], the KNN-based ensemble method is proposed and performance is evaluated.…”

Section: Structure and Literature Reviewmentioning

confidence: 99%

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis

K¹,

Jayanthy²

2022

Computational Intelligence and Neuroscience

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Stress is a universal emotion that every human experiences daily. Psychologists say stress may lead to heart attack, depression, hypertension, strokes, or even sudden death. Many technical explorations like stress detection through facial expression, speech, text, physical behaviors, etc., were explored, but no consensus has been reached on the best method. The advancement in biomedical engineering yielded a rapid development of electroencephalogram (EEG) signal analysis that has inspired the idea of a multimethod fusion approach for the first time which employs multiple techniques such as discrete wavelet transform (DWT) for de-noising, adaptive synthetic sampling (ADASYN) for class balancing, and affinity propagation (AP) as a stratified sampling model along with the artificial neural network (ANN) as the classifier model for human emotion classification. From the EEG recordings of the DEAP dataset, the artifacts are removed, the signal is decomposed using a DWT, and features are extracted and fused to form the feature vector. As the dataset is high-dimensional, feature selection is done and ADASYN is used to address the imbalance of classes resulting in large-scale data. The innovative idea of the proposed system is to perform sampling using affinity propagation as a stratified sampling-based clustering algorithm as it determines the number of representative samples automatically which makes it superior to the K-Means, K-Medoid, that requires the K-value. Those samples are used as inputs to various classification models, the comparison of the AP-ANN, AP-SVM, and AP-RF is done, and their most important five performance metrics such as accuracy, precision, recall, F1-score, and specificity were compared. From our experiment, the AP-ANN model provides better accuracy of 86.8% and greater precision of 85.7%, a higher F1 score of 84.9%, a recall rate of 84.1%, and a specificity value of 89.2% which altogether provides better results than the other existing algorithms.

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Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

Cited by 14 publications

References 55 publications

A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets

A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets

A k-Nearest Neighbours Based Ensemble via Optimal Model Selection for Regression

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis

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