Article
Multidisciplinary Sciences
Demeke Endalie, Tesfa Tegegne
Summary: A new dimension reduction approach combining feature selection and extraction is proposed to improve classification accuracy for Amharic digital documents. Experimental results show that this method outperforms others, achieving a classification accuracy of 92.60%. However, further work is needed to address the impact of reducing feature size on classification accuracy.
Article
Mathematics
Sebastian Alberto Grillo, Jose Luis Vazquez Noguera, Julio Cesar Mello Roman, Miguel Garcia-Torres, Jacques Facon, Diego P. Pinto-Roa, Luis Salgueiro Romero, Francisco Gomez-Vela, Laura Raquel Bareiro Paniagua, Deysi Natalia Leguizamon Correa
Summary: This study analyzes the impact of redundant features on classification model performance and proposes a theoretical framework for analyzing feature construction and selection. The experimental results suggest that a large number of redundant features can reduce the classification error.
Article
Biochemical Research Methods
Fengsheng Wang, Leyi Wei
Summary: In this study, we propose a novel multi-scale end-to-end deep learning model, MSTLoc, for identifying protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. We demonstrate that the proposed MSTLoc outperforms current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we show that the multi-scale deep features learned from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved in cancer development.
Article
Chemistry, Analytical
Maryam Assafo, Jost Philipp Staedter, Tenia Meisel, Peter Langendoerfer
Summary: Feature selection plays a crucial role in machine learning-based predictive maintenance applications. However, the stability of feature selection methods under data variations has not been fully addressed in the field of PdM. This paper investigates the stability and performance of three popular filter-based FS methods in tool condition monitoring.
Article
Mathematics
Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet, Akash Saxena, Karam M. Sallam, Ali Wagdy Mohamed
Summary: In this study, a chaos embed marine predator algorithm (CMPA) is proposed for feature selection in data mining applications. The comparative analysis and statistical significance tests provide evidence for the effectiveness and applicability of the proposed algorithm.
Article
Engineering, Multidisciplinary
Demeke Endalie, Getamesay Haile
Summary: The study introduces a hybrid feature selection method, IGCHIDF, which outperforms other methods on both datasets, particularly showing significant advantages on dataset 2.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhaogeng Liu, Jielong Yang, Li Wang, Yi Chang
Summary: In this paper, a novel wrapper feature selection method named ERASE is proposed, which learns and utilizes sample relations and feature relations for feature selection. Experimental results demonstrate that our method outperforms other feature selection methods in most cases.
PATTERN RECOGNITION
(2023)
Article
Automation & Control Systems
Xiaokang Wang, Huiwen Wang, Dexiang Wu
Summary: This study proposes an online dynamic feature weighting algorithm to monitor feature drift in data streams. The algorithm detects changes in class relevance of features based on the log-likelihood divergence score, and it has been shown to improve the accuracy rates of Nearest Neighbor and Naive Bayes classifiers on both synthetic and real-world datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Amy Hartman, Mahmoud Elkhadrawi, Sarah McKendry, Murat Akcakaya, Roxanna M. Bendixen
Summary: In this study, accelerometry and motor performance data were used to evaluate the effectiveness of a dynamic arm support device in individuals with Duchenne muscular dystrophy. The Support Vector Machine learning method was employed to accurately identify device usage and task success, showing promising results for remote monitoring of the device in a natural setting.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Lianxi Wang, Shengyi Jiang, Siyu Jiang
Summary: The study introduces a novel feature selection algorithm that selects relevant and interactive features using a maximum criterion, leading to improved classification accuracy. Experimental results show that the algorithm efficiently selects features and enhances classifiers to achieve better or comparable classification accuracy compared to ten representative competing feature selection algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Clinical Neurology
E. I. S. Hofmeijer, C. O. Tan, F. van der Heijden, R. Gupta
Summary: Researchers tested ensemble learning for selecting the best artificial intelligence models for intracranial hemorrhage detection, but ensemble learning methods did not outperform the single best model.
AMERICAN JOURNAL OF NEURORADIOLOGY
(2023)
Article
Computer Science, Information Systems
G. Wiselin Jiji
Summary: Algorithms in computer vision are crucial for extracting valuable hidden information from datasets. This study focuses on diagnosing neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and bipolar disorder. It uses potential biomarkers extracted from T1 MRI and brain tissue volumes, specifically the 3D Speeded Up Robust Feature (SURF) and 3D Scale Invariant Feature Transform (SIFT) features. Random Forest and SVM approaches are employed to select key points for diagnosis, achieving a classification accuracy of 98.6%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Amina Benkessirat, Nadjia Benblidia
Summary: In real-life classification applications, it can be challenging to select model features that adequately classify samples from a large number of candidates. This article's main contributions include evaluating the relevance and redundancy of features, defining the feature selection problem as an eigenvalue computation problem with a linear constraint, and efficiently selecting the best features. The approach was tested on 20 UCI benchmark datasets and compared with other widely used and state-of-the-art approaches. The experimental results showed that our approach improved the classification task by using only 20% of the conventional features.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Gazal, Kapil Juneja
Summary: This paper investigates a two-level filter-based hybrid model to accurately identify spam messages. The model selects the most important features through filtering and evaluation methods, and uses classifiers to generate probabilistic scores for spam detection. The experimental results show that the model achieves high accuracy on multiple datasets and outperforms traditional methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xi-Ao Ma, Hao Xu, Chunhua Ju
Summary: This paper proposes a class-specific feature selection method based on information theory. A class-specific feature evaluation criterion called CSMDCCMR is developed, and a feature selection algorithm is designed to select a suitable feature subset for each class. Experimental results demonstrate the superiority of the proposed method compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
