Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Buatoom, Uraiwan; Jamil, Muhammad Usman

doi:10.3390/app13032005

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…Machine learning algorithms play a vital role in diverse fields by enabling predictions and pattern discovery from vast datasets [21] - [23]. However, the curse of dimensionality presents challenges such as increased computational complexity and potential overfitting.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Dimensionality Reduction in Enhancing Machine Learning Algorithm Performance

Mutinda,

Langat

2024

AJRCoS

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In this study, we delve into the pivotal role of dimension reduction techniques in influencing the performance of machine learning algorithms for heart disease prediction. Through a comprehensive exploration of a dataset encompassing crucial features such as age, sex, chest pain type, blood pressure, cholesterol levels, and more, we investigate the impact of different techniques—namely, Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Linear Discriminant Analysis (LDA) on classification algorithm effectiveness. The classification algorithms considered were Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Naive Bayes, and Deep Neural Network (DNN). We used K-fold cross validation to train and validate the classification algorithms. The performance of these algorithms was assessed using a range of key metrics including accuracy, F1-score, precision, recall, and specificity. The results reveals that Linear Discriminant Analysis consistently emerged as a potent method, remarkably enhancing algorithm performance across all assessed metrics. We also identified Naive Bayes and Logistic Regression as standout algorithms, demonstrating remarkable resilience and reliability across diverse scenarios. These findings collectively shed light on the intricate interplay between dimension reduction techniques and algorithm selection, offering critical insights for crafting more accurate and robust strategies in the prediction of heart disease.

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Section: Introductionmentioning

confidence: 99%

Exploring the Role of Dimensionality Reduction in Enhancing Machine Learning Algorithm Performance

Mutinda,

Langat

2024

AJRCoS

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“…Further research showed that near the optimal drilling operating mode, the accompanying measured signal has typical identifiable and classifiable characteristics. A recent study [16] proposed a technique to improve the feature extraction capability before using the PCA method for feature selection. The authors of the manuscript deal with the scientific topic of effective control of the drilling process based on the classification of vibration signals from the aggregates of the drilling stand.…”

Section: Introductionmentioning

confidence: 99%

Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates

et al. 2023

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Rotary drilling technology with diamond tools is still essential in progressively extracting the earth’s resources. Since investigating the disintegration mechanism in actual conditions is very difficult, the practice must start with laboratory research. Identifying and classifying the drilling stand and its aggregates as objects will contribute to the clarification of certain problems related to streamlining the process, optimizing the working regime, preventing emergencies, and reducing energy and economic demands. For these purposes, the cluster method was designed and applied. Applying the clustering method has a significant place in complex and dynamic processes. Eight vibration signals were measured and processed during the operation of the aggregates, such as the motor, pump, and hydrogenerator, with a sampling frequency of 18 kHz and a time interval of 30 s. Subsequently, 16 symptoms were designed and numerically calculated in the time and frequency domain, creating the symptom vector of the aggregate. The aim of the study and article was the classification of aggregates as objects into recognizable clusters. The results show that the strong symptoms include a measure of variability, variance in the signal, and kurtosis. The weak symptoms are skewness and the moment of the signal spectrum. Visualization in the symptom plane and space proved their influence on cluster formation. According to the cluster analysis results, six to seven clusters presenting the activity of the aggregates were classified. It was found that the boundaries between the clusters were not sharp. As part of the research, the centroids of clusters of aggregates and the distances between them were calculated. Classified clusters can rebuild reference clusters for objects with a similar character in a broader context.

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Enhancing Web Security with a Blockchain-Powered Machine Learning Model for Predicting Malicious Web Domains

Yanqiu,

Goyal,

Rajawat

et al. 2024

Lecture Notes in Networks and Systems

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Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

Cited by 10 publications

References 35 publications

Exploring the Role of Dimensionality Reduction in Enhancing Machine Learning Algorithm Performance

Exploring the Role of Dimensionality Reduction in Enhancing Machine Learning Algorithm Performance

Application of Cluster Analysis for Classification of Vibration Signals from Drilling Stand Aggregates

Enhancing Web Security with a Blockchain-Powered Machine Learning Model for Predicting Malicious Web Domains

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