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HSS: enhancing IoT malicious traffic classification leveraging hybrid sampling strategy
Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of...
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Hybrid sampling feature enhancement: a few-shot learning method for substation equipment fault recognition
Due to the small sample size and unbalanced distribution, fault recognition of substation equipment becomes difficult. A few-shot feature enhancement...
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An imbalanced ensemble learning method based on dual clustering and stage-wise hybrid sampling
Imbalanced data classification remains a research hotspot and a challenging problem in the field of machine learning. The challenge of imbalanced...
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A hybrid sampling and gradient attention network for compressed image sensing
Block compressed sensing (BCS), which is widely used in compressed image sensing (CIS), brings the advantages of lower complexity for sampling and...
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Designing a modified feature aggregation model with hybrid sampling techniques for network intrusion detection
Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of...
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Asynchronous gain-scheduled control of deepwater drilling riser system with hybrid event-triggered sampling and unreliable communication
This paper investigates the recoil control of the deepwater drilling riser system with nonlinear tension force and energy-bounded friction force...
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Towards hybrid over- and under-sampling combination methods for class imbalanced datasets: an experimental study
The skewed class distributions of many class imbalanced domain datasets often make it difficult for machine learning techniques to construct...
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An Improved Hybrid Sampling Model for Network Intrusion Detection Based on Data Imbalance
Network intrusion detection constitutes a pivotal element in safeguarding computer networks against malicious attacks and unauthorized access. With...
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Constructing small sample datasets with game mixed sampling and improved genetic algorithm
The issue of categorizing imbalanced data is becoming increasingly prevalent. While existing methodologies have demonstrated notable advancements in...
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A new electricity theft detection method using hybrid adaptive sampling and pipeline machine learning
Electricity theft not only results in higher electricity costs for regular paying customers but is also a safety threat to the public due to illegal...
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CS- and GA-based hybrid evolutionary sampling algorithm for large-scale social networks
Social networks analysis (SNA) has been prevalent in the research community for decades. The challenges of SNA study include the massive quantity and...
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Evidential Hybrid Re-sampling for Multi-class Imbalanced Data
Learning from class-imbalanced datasets has gained substantial attention in the machine learning community, leading to solutions for healthcare,...
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Noise-free sampling with majority framework for an imbalanced classification problem
Class imbalance has been widely accepted as a significant factor that negatively impacts a machine learning classifier’s performance. One of the...
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A Novel Hybrid Sampling Method ESMOTE+SSLM for Handling the Problem of Class Imbalance with Overlap in Financial Distress Detection
The financial distress detection of listed companies is very important because it can prevent investors, managers and regulators from suffering huge...
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A hybrid model: PNM for improving prediction capability of classifier
In recent years, the COVID-19 and its variant are more dangerous for people with some health complexity, such as breast cancer, diabetes, and heart...
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A hybrid deep convolutional neural network model for improved diagnosis of pneumonia
Pneumonia is an infection that inflames the air sacs in lungs and is one of the prime causes of deaths under the age of five, all over the world....
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Dimension-independent spectral gap of polar slice sampling
Polar slice sampling, a Markov chain construction for approximate sampling, performs, under suitable assumptions on the target and initial...
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Empirical characterization of graph sampling algorithms
Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the...
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Random Subspace Sampling for Classification with Missing Data
Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully...