Analysis of DNA Sequence Classification Using SVM Model with Hyperparameter Tuning Grid Search CV

Mangkunegara, Iis Setiawan; Purwono, Purwono

doi:10.1109/cyberneticscom55287.2022.9865624

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The rationale behind this decision was to sample some of the genes that we had worked on in our previous research endeavors [15], Our objective was to integrate automated learning algorithms and pattern-matching algorithms that are based on specific DNA sequences, in order to create a biological data collection that could be utilized in a classification process. We conducted experiments on a dataset that included DNA sequences, where we compared the effectiveness of searching for a specific pattern with other classification models, such as Random Forest [3,16], KNN [16][17][18][19][20], Naïve Bayes [21][22][23][24], Decision tree [23,[25][26][27][28][29][30], and Support Vector Machine [18,[31][32][33][34][35][36] with Linear [37,38], RBF [37,39], and sigmoid [21,40] classifiers, the results of these classifiers models are calculated by F1 score, recall, precision rate, execution time, and with the accuracy which calculates the most effective patternmatching classifier. The comparison of DNA sequences is a crucial task in various fields of research, including molecular biology and genetics.…”

Section: Methodology For Pm From Dna Sequencesmentioning

confidence: 99%

Optimizing classification efficiency with machine learning techniques for pattern matching

Hamed¹,

Ibrahim²,

El‐Hafeez³

2023

J Big Data

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The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. The results show that as the pattern length increases, the execution time of each algorithm varies. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.

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Section: Methodology For Pm From Dna Sequencesmentioning

confidence: 99%

Optimizing classification efficiency with machine learning techniques for pattern matching

Hamed¹,

Ibrahim²,

El‐Hafeez³

2023

J Big Data

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“…It is widely used in data classification, regression fitting, and outlier detection. SVM has been successful in many practical diagnostic applications due to its ability to generalize and produce accurate predictions [31][32][33][34]. It is memory-efficient and can handle large datasets with ease.…”

Section: Multi-classification Fault Diagnosis Methods Based On Svmmentioning

confidence: 99%

Research on Fault Diagnosis of HVAC Systems Based on the ReliefF-RFECV-SVM Combined Model

Nie

Ren

et al. 2023

Actuators

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A fault diagnosis method of heating, ventilation, and air conditioning (HVAC) systems based on the ReliefF-recursive feature elimination based on cross validation-support vector machine (ReliefF-RFECV-SVM) combined model is proposed to enhance the diagnosis accuracy and efficiency. The method initially uses ReliefF to screen the original features, selecting those that account for 95% of the total weight. The recursive feature elimination based on cross validation (RFECV), based on a random forest classifier, is then applied to select the optimal feature subset according to diagnostic accuracy. Finally, a support vector machine (SVM) model is constructed for fault classification. The method is tested on seven typical faults of the ASHRAE 1043-RP water chiller dataset and three typical faults of an air-cooled self-built air conditioner simulation dataset. The results show that the ReliefF-RFECV-SVM method significantly reduces diagnosis time compared to SVM, shortening it by about 50% based on the ASHRAE 1043-RP dataset, while achieving an overall accuracy of 99.98%. Moreover, the proposed method achieves a comprehensive diagnosis accuracy of 99.97% on the self-built simulation dataset, with diagnosis time the reduced by about 65% compared to single SVM.

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“…Data processing is an important step before data can be applied to machine learning [26]. This step is used to ensure that the data is of good quality when used to train the model [27].…”

Section: Image Preprocessingmentioning

confidence: 99%

Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation

Purwono,

Mangkunegara

2023

IJRCS

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A brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance.

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Analysis of DNA Sequence Classification Using SVM Model with Hyperparameter Tuning Grid Search CV

Cited by 8 publications

References 26 publications

Optimizing classification efficiency with machine learning techniques for pattern matching

Optimizing classification efficiency with machine learning techniques for pattern matching

Research on Fault Diagnosis of HVAC Systems Based on the ReliefF-RFECV-SVM Combined Model

Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation

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