An Analysis of Computational Complexity and Accuracy of Two Supervised Machine Learning Algorithms—K-Nearest Neighbor and Support Vector Machine

Ray, Susmita

doi:10.1007/978-981-15-5616-6_24

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…The complexity of SVM has been well explored and confirmed. Referring to several pieces of research [44,45], we concluded that the complexity of linear SVM would be O(d) and that of RBF-SVM would be O d 2 , where d denotes the dimension of input.…”

Section: Discussionmentioning

confidence: 98%

A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

Chen

Lin

et al. 2022

Symmetry

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The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.

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Section: Discussionmentioning

confidence: 98%

A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

Chen

Lin

et al. 2022

Symmetry

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“…1TBA functionalized nanopapers failed to distinguish lactose but showed an acceptable accuracy for classifying the other glycans. When the number of the classification groups increases, the computation complexity of machine learning classifier increases dramatically 42 . To evaluate the limitation of the machine learning classifier, we analyzed all of the spectra previously collected from 11 sample groups, including 7 monosaccharides, maltose, isomaltose, lactose, and the mixture of galactose and glucose.…”

Section: Enhancing Classification Accuracy Using the Collective Sers ...mentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Preprint

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Glycans are the most abundant and diverse fundamental biomolecules. Profiling glycans is essential to elucidate biomolecular mechanisms and to predict biological consequences; however, the analysis of glycans remains challenging due to their structural complexity. A novel glycan detection platform is established through the integration of surface-enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. SERS is able to distinguish isomeric structures and provide fingerprint information of molecules. Boronic acid receptors can selectively bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. For proof-of-concept, two boronic acids, 4-mercaptophenylboronic acid (4MBA) and 1-thianthrenylboronic acid (1TBA) were used for glycan detection. The spectra were analyzed by the machine learning algorithm. The sensing platform successfully recognized the stereoisomers (glucose, mannose, and galactose), the structural isomers with α-1,4 and α-1,6 glycosidic linkages, and the β-1,4 glycosidic linkage within lactose molecules. The collective spectra that combine the spectra from both boronic acid receptors could improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition to qualitative analysis, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. The quantification accuracy reached a coefficient of determination (R2) value of 0.998 and a normalized mean square error (NMSE) of 0.00195. This low-cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent glycan screening in standard biological laboratories.

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“…These findings show that the circuit can solve linear systems quickly in a wide range of applications, indicating that IMC is a promising choice for future big data and machine learning accelerators. Ray [11] demonstrates that the complexity of KNN and SVM is analyzed, and other complexity-reducing methodologies are then examined. It is also suggested to use a hybrid method that combines KNN and SVM to use the possibilities of the two techniques.…”

Section: Related Workmentioning

confidence: 99%

“…To test the model's performance, a new equivalent dataset with only the input characteristics is supplied, and the goal of the model is to predict the target variable using the knowledge gathered during training. The expected values are compared to the actual target values using an appropriate performance metric [11,13].…”

Section: Machine Learningmentioning

confidence: 99%

A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity

Gzar¹,

Mahmood²,

Abbas³

2022

MMEP

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The computational complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers which is the determination of looking for the best methods to solve a problem. The accuracy of a regression model's predictions must be reported as an error. According to the researchers, the most problematic issue is the lack of a properly defined machine learning assessment. In this research, Various types of machine learning regression algorithms, namely, Linear Regression, Support Vector Regression, Random Forest Regression, and Multilayer Perceptron Neural Network have been used to process and analyze the collected data in terms of comparison of their accuracy and the computational complexity. The applied dataset was collected using IoT sensors seeking an appropriate algorithm that is the fittest to the collected data to design a model system that represents the goal of specific future applications. The result shows that the Random Forest regression has the highest computational complexity and highest accuracy depending on the calculated error metrics (Mean Square Error, Mean Absolute Error, and R Squared score) which are (0.0002, 0.005, and 0.995) respectively. Based on that, Random Forest Regression will be adapted and implemented with the structure of a planned design system.

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An Analysis of Computational Complexity and Accuracy of Two Supervised Machine Learning Algorithms—K-Nearest Neighbor and Support Vector Machine

Cited by 15 publications

References 7 publications

A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity

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