Microarray‐based cancer diagnosis: repeated cross‐validation‐based ensemble feature selection

Guney, Huseyin; Öztoprak, Hüseyin

doi:10.1049/el.2017.4550

Cited by 10 publications

(31 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It decomposes the data set into k folds so that each fold can be used in the training and testing phase. The performances of this method are presented in the work of Güney and Öztoprak [30].…”

Section: Realization Of the Inference Attackmentioning

confidence: 99%

“…This process was repeated to involve each fold in the training and testing phases. In our study, we used a variation of this method which is repeated cross-validation, and which offers better performance [30] compared to cross-validation. It consists of repeating the cross-validation process several times.…”

Section: Training and Testing Phasementioning

confidence: 99%

“…The authors [35][36][37] recommended a value of k in the range from 5 to 10. However, other values were used [30,38]. This leads us, for our study, to conduct several experiments to choose the best hyperparameters (number of folds and number of repetitions).…”

Section: Training and Testing Phasementioning

confidence: 99%

See 2 more Smart Citations

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Johnson¹,

Adépo²,

Kamagate³

et al. 2022

ISI

View full text Add to dashboard Cite

Data warehouses are environments used for data analysis and efficient decision making within companies. They are tools that allow the execution of complex and multidimensional queries. One of the security vulnerabilities that can be used by malicious users is data inference, which is the deduction of private information by devious means. In the present work, we tried to show that the existence of functional dependencies in the data can help to perform an inference attack by using supervised learning algorithms to infer private information. These algorithms are Support Vector Machine (SVM), Random Forest (RF), Bayesian Regularized Neural Network (BRNN) and K-Nearest Neighbors (K-NN). The BRNN provided a better performance in our study. This paper implements an inference attack using regression learning algorithms, studies different dependency situations in the data, and uses the combination of COUNT, SUM, AVG and STDEV queries. The use of several methods in this study allows the prevention of inferences when one of these methods is used by a malicious user. We managed to achieve this attack by detecting 09.12% inferences on all methods compared to BRNN whose realized inference rate is 03.94%.

show abstract

Section: Realization Of the Inference Attackmentioning

confidence: 99%

Section: Training and Testing Phasementioning

confidence: 99%

See 1 more Smart Citation

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Johnson¹,

Adépo²,

Kamagate³

et al. 2022

ISI

View full text Add to dashboard Cite

show abstract

“…Güneyet.al, [10] implemented a heuristic optimization model for PSO for micro array datasets. This model is applicable to limited feature space and limited number of instance in each training data.…”

Section: Related Workmentioning

confidence: 99%

Hybridization of PSO-ABC Based Ensemble Classification Model for High Dimensional Medical Datasets

Kumari

Rao²

2019

International Journal of Simulation: Systems, Science &Amp; Technology

View full text Add to dashboard Cite

As the size and dimensionality of microarray datasets increase, it is vital to select essential features for data classification. Traditionally, ranking and selection measures are used to select the essential features from the high dimensional feature space. However, these measures are used to improve the data classification rate with limited number of instances and features space. Feature selection is one of the challenging issues for microarray datasets due to noise, sparsity and missing values. Traditional feature selection models such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used to select the highly weighted features for data classification. But these models require high computational memory and time for data classification. In this paper, a hybrid PSO+ABC based feature selection model is designed and implemented on microarray disease datasets. Proposed hybrid feature selection model is applied on multiple classification models to improve the true positive rate and error rate for different dimensional datasets. Experimental results are simulated on different microarray disease dataset and these results proved that the hybrid feature selection model has high true positive rate and minimal mean squared error rate compared to the traditional models.

show abstract

“…2 Therefore, the multivariate FS techniques are more suitable for microarray datasets. 3 FS techniques broadly are categorized into three groups; filter, embedded, and wrapper techniques. 2 Also, some hybrid methods were created as a combination of any of the above-mentioned techniques.…”

Section: Introductionmentioning

confidence: 99%

A robust ensemble feature selection technique for high‐dimensional datasets based on minimum weight threshold method

Guney¹,

Öztoprak

2022

Computational Intelligence

View full text Add to dashboard Cite

Ensemble feature selection (EFS) is a valuable technique for developing accurate and robust machine-learning (ML) models. Data variation plays a crucial role in the success of EFS models; however, it also causes some outliers in the ranked lists. In this study, we proposed the minimum weight threshold method-based EFS (MWT-EFS) to address the outlier problem and use the true power of EFS. The proposed method employs the support vector classifier to assign weights for features, and the MWT method handles outliers in the ranked feature lists while creating the ensemble list. First, a threshold value is determined. After that, the feature weights below the threshold are replaced with this value. This approach eliminates the negative effect of outliers. After the new feature weights are assigned, the average of the feature weights is calculated (mean aggregation) for all features, and the ensemble (final) feature list is created accordingly. The experiment results showed that the proposed method significantly improves gene selection stability while maintaining classification performance and reducing computational complexity. In conclusion, the proposed method led to an accurate and robust classification that can help domain experts to make 1616

show abstract

Microarray‐based cancer diagnosis: repeated cross‐validation‐based ensemble feature selection

Cited by 10 publications

References 5 publications

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Inference Control in Aggregation Query Processing Based on Supervised Learning Models

Hybridization of PSO-ABC Based Ensemble Classification Model for High Dimensional Medical Datasets

A robust ensemble feature selection technique for high‐dimensional datasets based on minimum weight threshold method

Contact Info

Product

Resources

About