Effective Feature Selection Research Articles

Bio inspired Boolean artificial bee colony based feature selection algorithm for sentiment classification

Over the past few years, sentiment analysis has become most predominant aspect to be extracted in the modern digital era. Due to digitization all over the world, the number of existing digital documents increasing exponentially day by day. Analyzing such documents to extract the exact meaning itself becomes a tedious task. And analyzing those documents in sentimental point of view has increased the modality of difficultness to another level. In this research work, an effective bio-inspired filter-based feature selection algorithm is proposed to effectively handle the documents from sentiment point of view. A popular bio inspired algorithm called Artificial Bee Colony algorithm is modified using Boolean operators to effectively handle feature selection problem in classifying the documents from sentimental aspect. A total of 9 different documents instances are taken for evaluating the performance of proposed model. A total of 5 different filter-based measures are used to quantify the working model of the algorithm. The outputs are analyzed to state-of-the-art algorithms, and the comparative results demonstrate a substantial improvement over the existing models.

Joint Sparse Locality Preserving Regression for Discriminative Learning

Ridge Regression (RR) is a classical method that is widely used in multiple regression analysis. However, traditional RR does not take the local geometric structure of data into consideration for discriminative learning and it is sensitive to outliers as it is based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -norm. To address this problem, this article proposes a novel method called Joint Sparse Locality Preserving Regression (JSLPR) for discriminative learning. JSLPR not only applies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm on both loss function and regularization term but also takes the local geometric structure of the data into consideration. The use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm can guarantee the robustness to outliers or noises and the joint sparsity for effective feature selection. Taking the local geometric structure into consideration can improve the performance of the feature extraction and selection method when the data lie on a manifold. To solve the optimization problem of JSLPR, an iterative algorithm is proposed and the convergence of the algorithm is also proven. Experiments on four famous face databases are conducted and the results show the merit of the proposed JSLPR on feature extraction and selection.

Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots

Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful. Methods: In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques. Results: Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance. Conclusion: It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.

Gait disorder classification based on effective feature selection and unsupervised methodology

In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients’ progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients’ progress. The dataset in this study includes 84 measurements of 37 patients based on a physician’s opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key-point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator–discriminator networks (ET-GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high-level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi-loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

Detecting cyberthreats in Metaverse learning platforms using an explainable DNN

The rapid integration of the Internet of Artificial Intelligence and Internet of Things (AI-IoT) technologies has given rise to a pivotal element of the upcoming digital era, the Metaverse. This confluence has significantly impacted virtual learning platforms by introducing enhanced, immersive, and interactive environments for learners, educators, and institutions. However, the growing reliance on Metaverse necessitates robust cybersecurity measures to detect and mitigate cyber threats and ensure the safety of users. This paper proposes an explainable deep neural network (DNN) designed to identify and address network intrusion attacks within Metaverse learning environments. By leveraging the latest cybersecurity IoT datasets and employing an effective feature selection method, this study provides a visually interpretable, trustworthy, and quantitative explanation of the network intrusion detection system (NIDS) model using Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) explainability methods. The adopted explainable DNN, capable of processing network traffic features from interconnected Metaverse devices and IoT sensors, can facilitate accurate and comprehensible remediation of anomalous from benign activities within Metaverse. The NIDS model yields a high performing accuracy of 99.9% for establishing a more secure and trustworthy metaverse learning environment.

Multi-label feature selection with global and local label correlation

In various application domains, high-dimensional multi-label data has become more prevalent, presenting two significant challenges: instances with high-dimensional features and a large number of labels. In the context of multi-label feature selection, the objective is to choose a subset of features from a given set that is highly pertinent for predicting multiple labels or categories associated with each instance. However, certain characteristics of multi-label classification, such as label dependencies and imbalanced label distribution, have often been overlooked although they hold valuable insights for designing effective multi-label feature selection algorithms. In this paper, we propose a feature selection model which exploits explicit global and local label correlations to select discriminative features across multiple labels. In addition, by representing the feature matrix and label matrix in a shared latent space, the model aims to capture the underlying correlations between features and labels. The shared representation can reveal common patterns or relationships that exist across multiple labels and features. An objective function involving L2,1-norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection. The proposed method was evaluated on 14 real-world multi-label datasets using six evaluation metrics, through comprehensive experiments. The results indicate its effectiveness, surpassing that of several representative methods.

Partial multi-label learning via robust feature selection and relevance fusion optimization

Partial Multi-Label Learning (PML) is a more practical learning paradigm, in which the labeling information is ambiguated. Most existing PML algorithms rely on assumptions to resolve ambiguity. However, these assumptions do not account for the origin of the noise labeling and therefore fail to address the impact of noise on the learner’s performance at the root. In this paper, we will propose a PML method jointly granular ball-based robust feature selection and relevance fusion optimization (PML-GR). Specifically, in the first stage, we construct a granular ball to compute the core-set with weights and then design a feature importance evaluation function to assign weights to each feature in the core-set, resulting in a ranking of feature importance for the PML learner; in the second stage, based on the selected features, a fusion-based objective function is constructed to compute the label confidence by taking into account the joint effect of the global sample similarity and local label relevance. Finally, a multi-label prediction model is learned by fitting the multi-output regressor to the label confidence. The experimental results demonstrate that the proposed method achieves competitive generalization performance by effective feature selection and relevance fusion optimization, which can focus more on discriminative features and minimize the effect of noisy labels during training.

Crop Growth Prediction using Ensemble KNN-LR Model

Research in agriculture is expanding. Agriculture in particular relies heavily on earth and environmental factors, such as temperature, humidity, and rainfall, to forecast crops. Crop prediction is a crucial problem in agriculture, and machine learning is an emerging study area in this area. Any grower is curious to know how much of a harvest he can anticipate. In the past, producers had control over the selection of the product to be grown, the monitoring of its development, and the timing of its harvest. Today, however, the agricultural community finds it challenging to carry on because of the sudden shifts in the climate. As a result, machine learning techniques have increasingly replaced traditional prediction methods. These techniques have been employed in this research to determine crop production. It is critical to use effective feature selection techniques to transform the raw data into a dataset that is machine learning compatible in order to guarantee that a particular machine learning (ML) model operates with a high degree of accuracy. The accuracy of the model will increase by reducing redundant data and using only data characteristics that are highly pertinent in determining the model's final output. In order to guarantee that only the most important characteristics are included in the model, it is necessary to use optimal feature selection. Our model will become overly complex if we combine every characteristic from the raw data without first examining their function in the model-building process. Additionally, the time and area complexity of the Machine learning model will grow with the inclusion of new characteristics that have little impact on the model's performance. The findings show that compared to the current classification method, an ensemble technique provides higher prediction accuracy.

An ensemble learning-based feature selection algorithm for identification of biomarkers of renal cell carcinoma.

Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

NORMALIZATION AND FEATURE SELECTION USING ENSEMBLE METHODS FOR CROP YIELD PREDICTION

In machine learning study proposes an ensemble-based strategy for both feature selection and data standardization to enhance model performance and interpretability. To maintain consistency across datasets, it employ average filling and weighted K-means clustering. Weighted K-means assigns distinct values to samples based on their distances to cluster centers, offering a more precise representation of the data distribution. Meanwhile, average filling replaces missing values with the average of corresponding features, ensuring a complete dataset for subsequent analysis. For feature selection, adopt an ensemble approach that combines Random Forest (RF) with Logistic Regression (LR) and ElasticNet. RF captures feature importance through tree-based analysis, while LR and ElasticNet provide additional insights into feature relevance and coefficients. This amalgamation aims to provide a comprehensive understanding of feature importance within the dataset. Principal Component Analysis (PCA) is employed to reduce dataset complexity while preserving key properties, facilitating more effective feature selection. By identifying orthogonal components that best explain data variation, PCA enables efficient representation and feature selection. In the final stage, Support Vector Machines (SVM) are utilized for categorization. SVM, a powerful classification method, establishes strong decision boundaries that optimize the gap between classes. Leveraging the selected features, the SVM model effectively categorizes new instances.

Enhancing DDoS detection in SDIoT through effective feature selection with SMOTE-ENN.

Internet of things (IoT) facilitates a variety of heterogeneous devices to be enabled with network connectivity via various network architectures to gather and exchange real-time information. On the other hand, the rise of IoT creates Distributed Denial of Services (DDoS) like security threats. The recent advancement of Software Defined-Internet of Things (SDIoT) architecture can provide better security solutions compared to the conventional networking approaches. Moreover, limited computing resources and heterogeneous network protocols are major challenges in the SDIoT ecosystem. Given these circumstances, it is essential to design a low-cost DDoS attack classifier. The current study aims to employ an improved feature selection (FS) technique which determines the most relevant features that can improve the detection rate and reduce the training time. At first, to overcome the data imbalance problem, Edited Nearest Neighbor-based Synthetic Minority Oversampling (SMOTE-ENN) was exploited. The study proposes SFMI, an FS method that combines Sequential Feature Selection (SFE) and Mutual Information (MI) techniques. The top k common features were extracted from the nominated features based on SFE and MI. Further, Principal component analysis (PCA) is employed to address multicollinearity issues in the dataset. Comprehensive experiments have been conducted on two benchmark datasets such as the KDDCup99, CIC IoT-2023 datasets. For classification purposes, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest (RF), and Multilayer Perceptron classifiers were employed. The experimental results quantitatively demonstrate that the proposed SMOTE-ENN+SFMI+PCA with RF classifier achieves 99.97% accuracy and 99.39% precision with 10 features.

Semi-supervised feature selection with soft label learning

With the rapid increase of high-dimensional data mixed with labelled and unlabelled samples, the semi-supervised feature selection technique has received much attention in recent years. However, most existing approaches ignore the fuzziness of the data. Moreover, many feature selection methods need to measure the relationships among all samples, which is inefficient and difficult to be applied to large-scale data. To address the problems mentioned above, we propose an effective semi-supervised feature selection with the soft label learning (SFS-SLL) method in this paper. Specifically, we first learn initial soft labels based on the local distance between samples and clustering centers using an efficient fuzzy C-means clustering. We propose a supervised semantic constraint to exploit labelled and unlabelled data using manual labels as the soft label learning guidance. Then, we propose a simple yet effective sparse regression model which integrates soft label learning and feature selection into a unified framework. Finally, we derive an effective optimization strategy based on the alternating direction method of multipliers (ADMM) to iteratively solve the formulated problem. Experiment results on several benchmark datasets show a performance improvement on feature selection accuracy and efficiency over compared methods.

Analyzing Sentiments using Optimized Novel Ensemble Fuzzy and DL based Approach with Efficient Feature Selection and Extraction Models

The most popular and active area of data mining study is sentiment analysis. Twitter is a crucial platform for collecting and distributing people’s thoughts, feelings, views, and attitudes regarding specific entities. There are several social media networks available today. In light of this, sentiment analysis in the Natural Language Processing (NLP) field became fascinating. Various methods for analyzing sentiment have been explored. However, improvements are still required regarding reliability and system efficacy. Additionally, user emotional expressions typically take the form of naturally occurring human-written textual data with numerous noises and ambiguities. The intricate contextual significance of sentiment expressions is difficult for present studies on sentiment analysis to precisely capture and interpret, particularly in linguistics with complex frameworks. To address these issues, we presented a new integrated fuzzy neural network. The proposed framework is developed for effective and efficient feature selection and hybrid approach based sentiment analysis. Ensemble of novel Deep Convolutional Neuro-Fuzzy Inference System (DCNFIS) and Deep Learning-based (DL) Long short-term memory neural network multilayer stacked bidirectional LSTM neural network analyzes the sentiments. The provided dataset is initially cleaned up and filtered out as part of preprocessing. Utilizing the preprocessed data, sentiment-based features are extracted using the inception-ResNet-V2 model. Then, the relevant features are selected by employing the Enhanced Reptile Search Algorithm (ERSA). The Al-Biruni Earth Radius (BER) optimization algorithm is used to optimize the hyperparameters of the ensemble approach, which analyzes the sentiment categories such as positive, negative, very positive, very negative, and neutral. Finally, an effectiveness assessment of the suggested and present classifiers is presented. Using three distinct research datasets, we conducted an experimental evaluation of the suggested model. While differentiating from the proposed approach, the proposed approach yields a greater performance of 98.97% recall, 99.06% precision, 99.13% accuracy and 99.01% F1-score. The experimental investigation analyzes that the proposed approach gains superior performance over existing approaches

Boosting and Comparing Performance of Machine Learning Classifiers with Meta-heuristic Techniques to Detect Code Smell

Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells. Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems. Neglecting them may lead to future software defects, rendering systems challenging to maintain, and eventually obsolete. Supervised machine learning techniques have emerged as valuable tools for classifying code smells without needing expert knowledge or fixed threshold values. Further enhancement of classifier performance can be achieved through effective feature selection techniques and the optimization of hyperparameter values. Aim: Performance measures of multiple machine learning classifiers are improved by fine tuning its hyperparameters using various type of meta-heuristic algorithms including swarm intelligent, physics, math, and bio-based etc. Their performance measures are compared to find the best meta-heuristic algorithm in the context of code smell detection and its impact is evaluated based on statistical tests. Method: This study employs sixteen contemporary and robust meta-heuristic algorithms to optimize the hyperparameters of two machine learning algorithms: Support Vector Machine (SVM) and k-nearest Neighbors (k-NN). The No Free Lunch theorem underscores that the success of an optimization algorithm in one application may not necessarily extend to others. Consequently, a rigorous comparative analysis of these algorithms is undertaken to identify the best-fit solutions for code smell detection. A diverse range of optimization algorithms, encompassing Arithmetic, Jellyfish Search, Flow Direction, Student Psychology Based, Pathfinder, Sine Cosine, Jaya, Crow Search, Dragonfly, Krill Herd, Multi-Verse, Symbiotic Organisms Search, Flower Pollination, Teaching Learning Based, Gravitational Search, and Biogeography-Based Optimization, have been implemented. Results: In the case of optimized SVM, the highest attained accuracy, AUC, and F-measure values are 98.75%, 100%, and 98.57%, respectively. Remarkably, significant increases in accuracy and AUC, reaching 32.22% and 45.11% respectively, are observed. For k-NN, the best accuracy, AUC, and F-measure values are all perfect at 100%, with noteworthy hikes in accuracy and ROC-AUC values, amounting to 43.89% and 40.83%, respectively. Conclusion: Optimized SVM exhibits exceptional performance with the Sine Cosine Optimization algorithm, while k-NN attains its peak performance with the Flower Optimization algorithm. Statistical analysis underscores the substantial impact of employing meta-heuristic algorithms for optimizing machine learning classifiers, enhancing their performance significantly. Optimized SVM excels in detecting the God Class, while optimized k-NN is particularly effective in identifying the Data Class. This innovative fusion automates the tuning process and elevates classifier performance, simultaneously addressing multiple longstanding challenges.

Hybrid MRMR-PCA BagDT - An Effective Feature Selection based Ensemble Model for Real-Time Intrusion Detection in IoT Environment

Hybrid MRMR-PCA BagDT - An Effective Feature Selection based Ensemble Model for Real-Time Intrusion Detection in IoT Environment

CWSCNet: Channel-Weighted Skip Connection Network for Underwater Object Detection.

Autonomous underwater vehicles (AUVs) equipped with the intelligent underwater object detection technique is of great significance for underwater navigation. Advanced underwater object detection frameworks adopt skip connections to enhance the feature representation which further boosts the detection precision. However, we reveal two limitations of standard skip connections: (1) standard skip connections do not consider the feature heterogeneity, resulting in a sub-optimal feature fusion strategy; (2) feature redundancy exists in the skip connected features that not all the channels in the fused feature maps are equally important, the network learning should focus on the informative channels rather than the redundant ones. In this paper, we propose a novel channel-weighted skip connection network (CWSCNet) to learn multiple hyper fusion features for improving multi-scale underwater object detection. In CWSCNet, a novel feature fusion module, named channel-weighted skip connection (CWSC), is proposed to adaptively adjust the importance of different channels during feature fusion. The CWSC module removes feature heterogeneity that strengthens the compatibility of different feature maps, it also works as an effective feature selection strategy that enables CWSCNet to focus on learning channels with more object-related information. Extensive experiments on three underwater object detection datasets RUOD, URPC2017 and URPC2018 show that the proposed CWSCNet achieves comparable or state-of-the-art performances in underwater object detection.

Learning Fine-Grained Information with Capsule-Wise Attention for Salient Object Detection

With the popularity of convolutional neural networks being used for salient object detection (SOD), the performance has been significantly improved. However, how to integrate crucial features for modeling salient objects needs further exploration. In this work, we propose an effective feature selection scheme to solve this task. Firstly, we provide a Simplified Atrous Spatial Pyramid Pooling (SASPP) module to lightweight the multi-scale features. Dealing with the SASSP features, we design a pixel-level local feature selection scheme named Multi-Scale Capsule-wise Attention (MSCA). It aggregates features from multi-scales by dynamic routing and helps the network to generate fine-grained prediction maps. In addition, we exploit holistic features by the Spatial-wise Attention and Channel-wise Attention (SA/CA) mechanisms, which adaptively extracts spatial or channel information. We also propose a Multi-crossed Layer Connections (MLC) structure in the upsampling stage, to fuse features from not only different levels but also different scales. The salient object prediction is performed in a coarse-to-fine manner. By conducting comprehensive experiments on five benchmark datasets, our method achieves the best performance when compared to existing state-of-the-art approaches.

SMART HOME AUTOMATION SYSTEM FOR ENERGY CONSUMPTION USING TENSORFLOW-BASED DEEP ENSEMBLE LEARNING

Over the past decades, the evolution of new wireless technology has led to increased attention toward Smart Home Automation Systems (SHAS). In the smart home, numerous smart devices are interconnected with the proliferation of the Internet of Things (IoT) technology to provide users with a more comfortable lifestyle. Prior research on the smart home system has enacted machine learning and deep learning techniques to forecast the consecutive activities in the smart home. This research paper aims to enhance the future decision-making of energy consumption with the assistance of environmental factors and home appliances by exploiting the Tensorflow-based deep ensemble learning technique. The enhancement of future decision-making in smart home automation systems primarily involves the classification of energy consumption levels from the knowledge of external environmental factors and energy consumption levels of home appliances through the phases of data preprocessing, feature selection, fuzzy logic-based data labeling, and finally, classification of energy consumption using TensorFlow based deep ensemble learning technique. The data obtained from the effective feature selection technique is subjected to labeling via the fuzzy logic system to classify the energy consumption of smart home appliances. Finally, this work classifies the level of energy consumption based on the labeled knowledge of smart home data using a tensorflow-based deep ensemble learning model. The experimental model implements the proposed deep ensemble learning model in the tensorflow framework, which improves the decision-making performance of energy utilization in the smart home system. Experimental results illustrate that the proposed deep ensemble learning model yields superior classification performance than the other baseline classifiers, such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) on the smart home dataset.

Low-voltage AC series arc fault detection based on Fisher-mutual information feature selection

The detection of multi-feature fusion is a crucial approach to address the issue of series arc fault detection. Effective feature selection plays a vital role in enhancing the accuracy of the classifier and reducing system complexity. In this study, a feature selection algorithm based on Fisher-mutual information is proposed to tackle the problem of feature selection in multi-feature fusion detection. This algorithm utilizes the characteristics of arc fault voltage source to construct a feature pool. The Fisher-score algorithm and mutual information algorithm are employed to construct an optimal feature subset. The feature subset undergoes rough selection by retaining key features of the classifier and fine selection by eliminating redundant features. Experimental results and comparisons with related methods demonstrate that the proposed feature selection method significantly enhances the classifier's recognition accuracy, reduces classification and recognition time, diminishes the feature dimension, and outperforms other existing methods.