Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions

This research focuses mainly on the binary class imbalance problem in data mining. It investigates the use of combined approaches of data and algorithmic level solutions. Moreover, it examines the use of swarm intelligence and population-based techniques to combat the class imbalance problem at all levels, including at the data, algorithmic, and feature level. It also introduces various solutions to the class imbalance problem, in which swarm intelligence techniques like Stochastic Diffusion Search (SDS) and Dispersive Flies Optimisation (DFO) are used. The algorithms were evaluated using experiments on imbalanced datasets, in which the Support Vector Machine (SVM) was used as a classifier. SDS was used to perform informed undersampling of the majority class to balance the dataset. The results indicate that this algorithm improves the classifier performance and can be used on imbalanced datasets. Moreover, SDS was extended further to perform feature selection on high dimensional datasets. Experimental results show that SDS can be used to perform feature selection and improve the classifier performance on imbalanced datasets. Further experiments evaluated DFO as an algorithmic level solution to optimise the SVM kernel parameters when learning from imbalanced datasets. Based on the promising results of DFO in these experiments, the novel approach was extended further to provide a hybrid algorithm that simultaneously optimises the kernel parameters and performs feature selection

Exploring Feature-Level Duplications on Imbalanced Data Using Stochastic Diffusion Search

One of the computer algorithms inspired by swarm intelligence is stochastic diffusion search (SDS). SDS uses some of the processes and techniques found in swarm to solve search and optimisation problems. In this paper, a hybrid approach is proposed to deal with real-world imbalanced data. The proposed model involves oversampling the minority class, undersampling the majority class as well as optimising the parameters of the classifier, Support Vector Machine (SVM). The proposed model uses Synthetic Minority Over-sampling Technique (SMOTE) to perform the oversampling and the agents of a swarm intelligence technique, SDS, to perform an `informed' undersampling on the majority classes. In addition to comparing the agents-led undersampling with random undersampling, the results are contrasted against other best known techniques on nine real-world datasets. Moreover, the behaviour of SDS agents in this context is also analysed

Handling Class Imbalance In Direct Marketing Dataset Using A Hybrid Data and Algorithmic Level Solutions

Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers, or charities wish to identify potential givers. Several solutions have been suggested in the literature to address this problem, amongst which are data-level techniques, algorithmic-level techniques and a combination of both. In this paper, a model is proposed to solve imbalanced data using a Hybrid of Data-level and Algorithmic-level solutions (HybridDA), which involves oversampling the minority class, undersampling the majority class, and additionally, optimising the cost parameter, the gamma and the kernel type of Support Vector Machines (SVM) using a grid search. The proposed model perfomed competitively compared with other models on the same dataset. The dataset used in this work are real-world data collected from a Portuguese marketing campaign for bank-deposit subscriptions and are available from the University of California, Irvine (UCI) Machine Learning Repository

Enhancing Workplace Safety: PPE_Swin—A Robust Swin Transformer Approach for Automated Personal Protective Equipment Detection

Accidents occur in the construction industry as a result of non-compliance with personal protective equipment (PPE). As a result of diverse environments, it is difficult to detect PPE automatically. Traditional image detection models like convolutional neural network (CNN) and vision transformer (ViT) struggle to capture both local and global features in construction safety. This study introduces a new approach for automating the detection of personal protective equipment (PPE) in the construction industry, called PPE_Swin. By combining global and local feature extraction using the self-attention mechanism based on Swin-Unet, we address challenges related to accurate segmentation, robustness to image variations, and generalization across different environments. In order to train and evaluate our system, we have compiled a new dataset, which provides more reliable and accurate detection of personal protective equipment (PPE) in diverse construction scenarios. Our approach achieves a remarkable 97% accuracy in detecting workers with and without PPE, surpassing existing state-of-the-art methods. This research presents an effective solution for enhancing worker safety on construction sites by automating PPE compliance detection