KDE-Based Ensemble Learning for Imbalanced Data

Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge Avante

doi:10.3390/electronics11172703

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…These are then used as input features for the meta-model, which subsequently determines the final prediction for that instance. The overarching goal of the stacking model is to capitalize on the strengths of each individual model, aiming to produce predictions that are more accurate than any single model could achieve on its own [40].…”

Section: Model Descriptionsmentioning

confidence: 99%

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Saidjon Kamolov

2024

AMCS

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Predicting the compressive strength of concrete provides several benefits including insights into its durability, potential lifespan, and its suitability for specific construction projects. Recent advances in machine learning algorithms have prompted their application to compressive strength modeling and prediction. In this paper, we carry out a comprehensive comparative analysis of the existing machine learning algorithms. Unlike, other current studies in this domain, we consider the entire spectrum of algorithms ranging from basic linear models to advanced meta-learning methods. The results show that there exists a trade-off between speed and accuracy of the models. We find that while ensemble models such as stacking and gradient boost provide superior accuracy, they are significantly slower than linear models.

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Section: Model Descriptionsmentioning

confidence: 99%

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Saidjon Kamolov

2024

AMCS

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“…Algorithm 1 illustrates the operation process of the KGSMOTE module. Firstly, the KDE [40] is used to estimate the probability density function of rareclass attack samples. Then, the generated rare-class attack data distribution is extracted from this probability density function, as shown in Equation (3).…”

Section: Imbalanced Data Processing Module Based On Kgsmotementioning

confidence: 99%

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Yang,

Gu,

Yan

2023

Electronics

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Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare-class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on the optimized kernel density estimation algorithm. This method can generate diverse rare-class attack data by learning the distribution of rare-class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft-voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules. The performance of the system was evaluated using two datasets, NSL-KDD and N-BaIoT, achieving 86.39% and 99.94% multiclassification accuracy, respectively. Through ablation experiments and comparison with the baseline model, it is found that the inherent limitations of a single machine-learning model directly affect the accuracy of the intrusion detection system, while the superiority of the proposed multi-module model in detecting unknown attacks and rare classes of attack traffic is demonstrated.

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“…Kernel density estimation (KDE) [17] is an unsupervised probability distribution learning method. In recent studies [18][19][20], it functions as a data analyst and classifier. The single KDE analyst fails to capture the features of data with many details and frequent variations such as symbols in NMN.…”

Section: Introductionmentioning

confidence: 99%

Kernel Density Estimation and Convolutional Neural Networks for the Recognition of Multi-Font Numbered Musical Notation

2022

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Optical music recognition (OMR) refers to converting musical scores into digitized information using electronics. In recent years, few types of OMR research have involved numbered musical notation (NMN). The existing NMN recognition algorithm is difficult to deal with because the numbered notation font is changing. In this paper, we made a multi-font NMN dataset. Using the presented dataset, we use kernel density estimation with proposed bar line criteria to measure the relative height of symbols, and an accurate separation of melody lines and lyrics lines in musical notation is achieved. Furthermore, we develop a structurally improved convolutional neural network (CNN) to classify the symbols in melody lines. The proposed neural network performs hierarchical processing of melody lines according to the symbol arrangement rules of NMN and contains three parallel small CNNs called Arcnet, Notenet and Linenet. Each of them adds a spatial pyramid pooling layer to adapt to the diversity of symbol sizes and styles. The experimental results show that our algorithm can accurately detect melody lines. Taking the average accuracy rate of identifying various symbols as the recognition rate, the improved neural networks reach a recognition rate of 95.5%, which is 8.5% higher than the traditional convolutional neural networks. Through audio comparison and evaluation experiments, we find that the generated audio maintains a high similarity to the original audio of the NMN.

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KDE-Based Ensemble Learning for Imbalanced Data

Cited by 12 publications

References 30 publications

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Kernel Density Estimation and Convolutional Neural Networks for the Recognition of Multi-Font Numbered Musical Notation

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