Optimizing classification efficiency with machine learning techniques for pattern matching

Abstract

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.

PMTPred: Machine Learning Based Prediction of Protein Methyltransferases using the Composition of k-spaced Amino Acid Pairs

“…It was observed that SVM performed well with the CKSAAP feature in comparison to other models (Supplementary Table S1). Due to good generalization ability, SVM has been effectively used in numerous classification problems [78][79][80]. Thus, the SVM-based CKSAAP model was selected as the final prediction model in this study.…”

Analysis of Machine Learning Approaches for DNA Sequencing and Classification: An optimized Approach

“…Machine learning is being used by many sectors to extract pertinent data from accessible datasets. A wide variety of algorithms have been developed to enable computers to learn on their own, with the primary goal of machine learning being the extraction of knowledge from available data [2][3][4] [5].…”

“…Osisanwo F.Y. et.al [5]. compares various supervised machine learning algorithms for classification.…”