Fine-Grained Algorithm for Improving KNN Computational Performance on Clinical Trials Text Classification

Jasmir, Jasmir; Nurmaini, Siti; Tutuko, Bambang

doi:10.3390/bdcc5040060

Cited by 8 publications

(4 citation statements)

References 22 publications

(31 reference statements)

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“…: random decimal from 0 to 1 After updating the global fitness, the next step carried out by PSO is to examine the updated result by comparing it with the particle fitness. If the particle fitness has a better value, PSO will update the best particle by applying the equation shown in (2).…”

Section: Combination Pso Knnmentioning

confidence: 99%

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Rizki,

Hermawan,

Avianto

2024

JUITA

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This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.

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Section: Combination Pso Knnmentioning

confidence: 99%

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Rizki,

Hermawan,

Avianto

2024

JUITA

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“…Handling this problem involves using text preprocessing algorithms through natural language processing (NLP) techniques to vectorize texts, fine-tuning hyperparameters of various classifiers algorithms [12][13][14] with Genetic Algorithms (GA), subgroup discovery algorithms [15], and statistical analysis methods to evaluate the effectiveness (accuracy) of the developed models. The complexity lies in the absence of accurate target labels in the training data, which are essential for classification methods and model training and tuning.…”

Section: Introductionmentioning

confidence: 99%

Application of Natural Language Processing and Genetic Algorithm to Fine-Tune Hyperparameters of Classifiers for Economic Activities Analysis

Malashin,

Masich,

Tynchenko

et al. 2024

BDCC

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This study proposes a method for classifying economic activity descriptors to match Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning techniques and expert evaluation. By leveraging natural language processing (NLP) methods to vectorize activity descriptors and utilizing genetic algorithm (GA) optimization to fine-tune hyperparameters in multi-class classifiers like Naive Bayes, Decision Trees, Random Forests, and Multilayer Perceptrons, our aim is to boost the accuracy and reliability of an economic classification system. This system faces challenges due to the absence of precise target labels in the dataset. Hence, it is essential to initially check the accuracy of utilized methods based on expert evaluations using a small dataset before generalizing to a larger one.

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“…With the increasing amount of data and data complexity as in the case above, deep learning offers an offer to solve this problem, with its high and almost perfect processing capabilities, deep learning [7] has achieved outstanding results in various fields, such as computer vision [8] , voice recognition [9] , and text classification [10] - [12].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Improvement Text Classification of Spam YouTube Video Comment using Deep Learning

Jasmir,

Riyadi,

Jusia

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments

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Fine-Grained Algorithm for Improving KNN Computational Performance on Clinical Trials Text Classification

Cited by 8 publications

References 22 publications

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization

Application of Natural Language Processing and Genetic Algorithm to Fine-Tune Hyperparameters of Classifiers for Economic Activities Analysis

Feature Extraction for Improvement Text Classification of Spam YouTube Video Comment using Deep Learning

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