Article
Computer Science, Information Systems
Bekir Parlak, Alper Kursat Uysal
Summary: In the domain of text classification, applying feature selection methods is essential for improving classification accuracy. This study proposes a new feature selection method called Extensive Feature Selector (EFS), which utilizes corpus-based and class-based probabilities. The performance of EFS is compared with nine other methods on four benchmark datasets, and the results show that EFS outperforms the other methods in most cases.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bekir Parlak
Summary: Text classification is a crucial task in this era of high-volume text datasets, and feature selection plays a key role in TC studies. Numerous feature selection methods are recommended in the literature, with filter-based methods commonly used. Each method scores and selects features based on its algorithm, choosing top-ranked features for the classification process.
COMPUTATIONAL INTELLIGENCE
(2023)
Review
Computer Science, Information Systems
Hong Ming, Wang Heyong
Summary: This paper provides a comprehensive systematic review of existing filter feature selection methods for text classification. It discusses mathematical designs, effectiveness, and complexity of different methodologies (supervised, unsupervised, and hybrid methods). Benchmark datasets for evaluating performance are also discussed. Future research directions and conclusions are provided.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
K. Thirumoorthy, K. Muneeswaran
Summary: Feature selection is crucial in reducing high dimensional feature space, and the proposed hybrid feature selection method based on binary poor and rich optimization algorithm outperforms other techniques in obtaining optimal feature subset for accurate text classification.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Information Systems
Adam Lysiak, Miroslaw Szmajda
Summary: This study compared nine feature evaluation methods by evaluating features from ten different datasets and training various classifiers. The results indicated that the method based on the average overlap between feature values is best suited for applications with limited computational power, while the two-sample t-test method may be preferable for datasets known to be normally distributed.
Article
Computer Science, Artificial Intelligence
Jefferson G. Martins, Luiz E. S. Oliveira, Daniel Weingaertner, Andersson Barison, Gerlon A. R. Oliveira, Luciano M. Liao
Summary: Forests are being exploited disorderly and many species are endangered, prompting the need for a spatial distribution plan. Researchers facing a lack of representative databases can benefit from introducing new databases and proposing selection strategies to improve outcomes.
Article
Computer Science, Information Systems
Husam Ali Abdulmohsin, Hala Bahjat Abdul Wahab, Abdul Mohssen Jaber Abdul Hossen
Summary: Feature selection is a crucial process in pattern recognition and machine learning, as it can improve classification speed and accuracy, and reduce system complexity. In this work, a new statistical method is proposed to select features based on performance value quality and recognition value strength, ranking features by final weight and removing those below a predefined threshold. Experiments show that the proposed method outperforms traditional FS methods in terms of system complexity and performance.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Review
Computer Science, Artificial Intelligence
Julliano Trindade Pintas, Leandro A. F. Fernandes, Ana Cristina Bicharra Garcia
Summary: The systematic literature review (SLR) assessed 1376 unique papers on feature selection methods in text classification published in the past eight years. Through mapping different aspects of proposed methods and identifying main characteristics of experiments, the SLR helps researchers develop new studies and position them in the context of existing literature.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
S. Eskandari, M. Seifaddini
Summary: This paper proposes a new approach for streaming feature selection by defining the redundancy analysis step as a binary optimization problem and adopting the binary bat algorithm to find the minimal informative subsets. Experimental studies show that this method outperforms other online and offline streaming feature selection methods in terms of classification accuracy.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Heba Mamdouh Farghaly, Tarek Abd El-Hafeez
Summary: The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. This work explores the use of association analysis in data mining to select meaningful features and proposes a novel feature selection technique for text classification. The technique effectively reduces redundant information while achieving high accuracy using only 6% of the features.
Article
Computer Science, Artificial Intelligence
Xiaodong Wang, Pengtao Wu, Qinghua Xu, Zhiqiang Zeng, Yong Xie
Summary: Traditional K-means struggles with high-dimensional data due to irrelevant features or noise, prompting recent studies to combine it with subspace learning. This study proposes an auto-adjoined subspace clustering approach, enhancing clustering performance through efficient feature selection and clear adjacency exploration.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Shao Liu, Jiaqi Yang, Sos S. Agaian, Changhe Yuan
Summary: The article introduces three novel features and a mature model structure for artistic movement recognition of portrait paintings, showing the successful application of these features in various neural networks through extensive evaluation. Additionally, a new portrait database containing 927 paintings from 6 different art movements is presented, demonstrating the superiority of the proposed method over state-of-the-art approaches.
IMAGE AND VISION COMPUTING
(2021)
Article
Computer Science, Information Systems
Bekir Parlak
Summary: Text classification is an important topic in the current era, but in feature selection, the information of the features is often ignored. This study proposes a new globalization technique, called FCWS, which considers both feature and class information to improve classification performance. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Applied
Valber Elias de Almeida, David Douglas de Sousa Fernandes, Paulo Henrique Goncalves Dias Diniz, Adriano de Araujo Gomes, Germano Veras, Roberto Kawakami Harrop Galvao, Mario Cesar Ugulino Araujo
Summary: This paper introduces a new algorithm, PCA-DP-LDA, for solving classification problems of food data using PCA and LDA. Compared with conventional methods, PCA-DP-LDA achieves more parsimonious and interpretable results, with similar or better classification performance.
Article
Computer Science, Artificial Intelligence
Mohsen Miri, Mohammad Bagher Dowlatshahi, Amin Hashemi, Marjan Kuchaki Rafsanjani, Brij B. Gupta, W. Alhalabi
Summary: The value and importance of multi-label text classification have increased due to the overgrowth of data. Preprocessing and intelligent feature selection are crucial steps in classification. This article proposes an ensemble feature selection method using order statistics to improve the accuracy of multi-label text classification.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Selcan Kaplan Berkaya, Serkan Gunal, Cuneyt Akinlar
PATTERN ANALYSIS AND APPLICATIONS
(2018)
Article
Engineering, Biomedical
Selcan Kaplan Berkaya, Alper Kursat Uysal, Efnan Sora Gunal, Semih Ergin, Serkan Gunal, M. Bilginer Gulmezoglu
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2018)
Article
Computer Science, Information Systems
Ozkan Aslan, Serkan Gunal, Bekir Taner Dincer
INFORMATION PROCESSING & MANAGEMENT
(2018)
Article
Computer Science, Information Systems
Bekir Parlak, Alper Kursat Uysal
JOURNAL OF INFORMATION SCIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Turgut Dogan, Alper Kursat Uysal
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Biology
Selcan Kaplan Berkaya, Ilknur Ak Sivrikoz, Serkan Gunal
COMPUTERS IN BIOLOGY AND MEDICINE
(2020)
Article
Computer Science, Information Systems
Bekir Parlak, Alper Kursat Uysal
Summary: This study comprehensively investigated the effects of various globalisation techniques on local feature selection methods, finding that the AVG method was the most successful across different dataset characteristics. The DFSS method performed well on MCU and MCB datasets, while the CHI2 method was more accurate on BCU and BCB datasets. Additionally, the SVM classifier outperformed the DT classifier in most cases.
JOURNAL OF INFORMATION SCIENCE
(2021)
Article
Computer Science, Information Systems
Bekir Parlak, Alper Kursat Uysal
Summary: In the domain of text classification, applying feature selection methods is essential for improving classification accuracy. This study proposes a new feature selection method called Extensive Feature Selector (EFS), which utilizes corpus-based and class-based probabilities. The performance of EFS is compared with nine other methods on four benchmark datasets, and the results show that EFS outperforms the other methods in most cases.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Ecology
Selcan Kaplan Berkaya, Efnan Sora Gunal, Serkan Gunal
Summary: Deep learning-based image classification models are proposed for beehive monitoring, capable of recognizing different conditions and abnormalities with an accuracy of up to 99.07%, making them good candidates for smart beekeeping and beehive monitoring.
ECOLOGICAL INFORMATICS
(2021)
Article
Computer Science, Hardware & Architecture
Muhammet Yasin Pak, Serkan Gunal
Summary: This paper proposes a rule mining model based on sequential patterns for cross-domain opinion target extraction from product reviews in unknown domains. Experimental results show that the proposed model can extract opinion targets more accurately than previous studies.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Rasim Cekik, Alper Kursat Uysal
Summary: Short texts are very common, especially in social media networks. The task of short text classification is crucial for various applications. However, using the entire feature set for classification can lead to high dimensionality and negatively impact classifier performance. In this study, the XY method is proposed as an effective feature selection algorithm. It calculates the distance of features to the XY line and uses the lambda value to determine the discriminatory capability of terms. Evaluation results show that the XY method achieves better or competitive performance while significantly reducing feature sizes.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Chemistry, Multidisciplinary
Mohamed Lemine Sidi, Serkan Gunal
Summary: This paper proposes a Purely Entity-based Semantic Search Approach for Information Retrieval (PESS4IR) to improve document retrieval. The approach includes its own entity linking and inverted indexing methods, as well as an appropriate ranking method. The experiments show that the approach achieves good performance on queries with rich annotations.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Huseyin Gunduz, Cuneyd Nadir Solak, Serkan Gunal
Summary: In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed. Through extensive experimental work, the performance of the proposed segmentation model is measured and verified to surpass previous works.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2022)
Article
Education & Educational Research
Derya Uysal, Alper Kursat Uysal
Summary: This study aims to use machine learning methods to classify EFL learners along an affective continuum and provides a new dataset about their affective characteristics. The results show that machine learning methods can effectively classify students' affective characteristics and feature selection methods can be used to assess their feelings towards EFL learning.
ADVANCED EDUCATION
(2022)
Article
Multidisciplinary Sciences
Turgut Dogan, Alper Kursat Uysal
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
