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检索条件"主题词=Classification Algorithms"

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classification depend on linear discriminant analysis using desired outputs

Classification depend on linear discriminant analysis using ...

引用

International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

作者： Nasar Aldian Ambark Shashoa Nuredin Ali Salem Ibrahim N. Jleta Omar Abusaeeda Electrical and Electronics Engineering Department Azzaytuna University Tripoli Libya Department of Electrical Engineering Libyan Academy of Graduate Studies Tripoli Libya Department of Computer Engineering Azzaytuna University Tripoli Libya

ISBN: (纸本)9781509034086

Linear Discriminant Analysis (LDA) is a popular technique in pattern recognition. This paper presents a linear discriminant analysis for classification. Firstly, linear discriminant Analysis is introduced. The optimum design procedure is demonstrated in order to calculate the error minimization. Next, linear classifier based on desired outputs is derived to get the desired classification. Also, the performance measure is described and calculated to show the effectiveness of the classification algorithm. The usefulness of the proposed procedures was proved in the simulation results.

关键词： Linear discriminant analysis Principal component analysis classification algorithms Covariance matrices Electronic mail Simulation Computers

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Fuzzy cluster ensemble and its application on 3D head model classification

Fuzzy cluster ensemble and its application on 3D head model ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zhiwen Yu Zhongkai Deng Hau-San Wong Xing Wang Department of Computer Science City university of Hong Kong Hong Kong China

In this paper, we propose a new algorithm called fuzzy cluster ensemble algorithm (FCEA) which integrates the fuzzy logic theory and traditional cluster ensembles for 3D head model classification. Specifically, FCEA consists of two parts: (i) data processing on the distributed locations and (ii) data fusion on the centralized location. In the distributed locations, data processing includes (i) extracting feature vectors from 3D head models, (ii) performing basic fuzzy clustering algorithm to obtain fuzzy membership matrix, while data fusion on the centralized location contains (i) creating a fuzzy cluster ensemble constructor by integrating different fuzzy membership matrices from the distributed locations, and (ii) obtaining the final results of 3D head model classification based on the fuzzy logic theory and the fuzzy cluster ensemble constructor. The experiments show that FCEA works well on 3D head model database.

关键词： Three dimensional displays Solid modeling Clustering algorithms Feature extraction classification algorithms Histograms Head

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Users classification on Broadcast and Television System Based on Statistical Analysis System Software

Users Classification on Broadcast and Television System Base...

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International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC)

作者： Binbin Wan Taozheng Zhang Jianping Chai Min Zhao Zhenlong Zhang Science and Engineering Dept. Communication University of China Beijing China

In this paper, we take the guide data and the program data from the users of digital cable television programs as the experimental data to carry on our experiment. And we use the SAS software platform which is very efficient in data analyzing to realize the classification of our user to different parts. So we can achieve our destination of personal recommendation and precision advertizing.

关键词： TV Synthetic aperture sonar Software Data mining Clustering algorithms Statistical analysis classification algorithms

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Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label classification

Multiobjective Fuzzy Genetics-Based Machine Learning for Mul...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Yuichi Omozaki Naoki Masuyama Yusuke Nojima Hisao Ishibuchi Department of Computer Science and Intelligent Systems Osaka Prefecture University Osaka Japan Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

In multi-label classification problems, multiple class labels are assigned to each instance. Two approaches have been studied in the literature. One is a data transformation approach, which transforms a multi-label dataset into a number of singlelabel datasets. However, this approach often loses the correlation information among classes in the multi-class assignment. The other is a method adaptation approach where a conventional classification method is extended to multi-label classification. Recently, some explainable classification models for multi-label classification have been proposed. Their high interpretability has also been discussed with respect to the transparency of the classification process. Although the explainability is a well-known advantage of fuzzy systems, their applications to multi-label classification have not been well studied. Since multi-label classification problems often have vague class boundaries, fuzzy systems seem to be a promising approach to multi-label classification. In this paper, we propose a new multiobjective evolutionary fuzzy system, which can be categorized as a method adaptation approach. The proposed algorithm produces nondominated classifiers with different tradeoffs between accuracy and complexity. We examine the behavior of the proposed algorithm using synthetic multi-label datasets. We also compare the proposed algorithm with five representative algorithms. Our experimental results on real-world datasets show that the obtained fuzzy classifiers with a small number of fuzzy rules have high transparency and comparable generalization ability to the other examined multi-label classification algorithms.

关键词： classification algorithms Fuzzy systems Machine learning algorithms Measurement Machine learning Correlation Fuzzy sets

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Enhancing classification Performance: A Study on SMOTE and Ensemble Learning Techniques

Enhancing Classification Performance: A Study on SMOTE and E...

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Future Technologies for Smart Society (ICFTSS), International Conference on

作者： Nadya Alinda Rahmi Sarjon Defit Okfalisa Information System Department Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Department Universitas Putra Indonesia YPTK Padang Indonesia Informatics Engineering Department Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798350373844

ISBN: (纸本)9798350373851

This research focuses on improving the performance of classification algorithms by employing the Synthetic Minority Over-sampling Technique (SMOTE) and ensemble learning strategies. SMOTE addresses the critical issue of class imbalance commonly encountered in classification tasks, while ensemble learning leverages the combined predictive power of multiple classifiers. Through a series of comprehensive experiments with various classification algorithms, this study demonstrates significant improvements in classification metrics such as accuracy, precision, recall, and F1-Score after integrating SMOTE and ensemble learning via Voting. The findings highlight the effectiveness of these techniques in enhancing model performance, underscoring their importance for practitioners and researchers in machine learning and data analytics. The novelty of this research lies in the combined application of SMOTE and ensemble learning, which provides a robust solution to class imbalance and enhances overall classification efficacy.

关键词： Measurement Analytical models Accuracy Data analysis Predictive models Data models Robustness classification algorithms Ensemble learning

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Towards A Binary classification Algorithm for the Prediction of Alzheimer’s Disease

Towards A Binary Classification Algorithm for the Prediction...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Aneesh Mazumder Anirudh Mazumder Harvard University Cambridge United States of America Texas Academy of Mathematics and Science Denton United States of America

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Alzheimer’s Disease (AD) is a significant global health challenge with a rising prevalence, affecting millions of individuals worldwide. With projections estimating a staggering increase in affected individuals, there is an urgent need for advanced diagnostic tools and a deeper understanding of AD’s underlying mechanisms. While diagnostic methods predominantly rely on imaging techniques like CT and MRI, utilizing postmortem human brain tissues offers a more intricate structural analysis. This study presents a novel approach to developing a binary classification algorithm for AD using a convolutional neural network (CNN) trained on postmortem tissue samples imaged via immunofluorescence microscopy. The focus on cilia, implicated in signaling deficiencies associated with AD, along with neuronal and astrocyte cell types, aims to capture distinct patterns linked to AD pathology. The CNN-based algorithm identifies structural patterns within tissue samples from AD and non-AD patients, facilitating the differentiation between the two groups. Leveraging CNN’s capacity to discern and extract intricate image patterns, this diagnostic tool seeks to enhance AD diagnosis and advance the understanding of the disease’s underlying mechanisms. By harnessing postmortem samples and the capabilities of CNNs, this study represents a promising step towards a more refined and potentially more accurate diagnostic tool for Alzheimer’s Disease, offering prospects for earlier and more precise detection, crucial in combating this progressive neurodegenerative ailment.

关键词： Brain Pathology Magnetic resonance imaging Medical services Machine learning Prediction algorithms classification algorithms

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EMG motion pattern classification through design and optimization of Neural Network

EMG motion pattern classification through design and optimiz...

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International Conference on Biomedical Engineering (ICoBE)

作者： Md. Rezwanul Ahsan Muhammad Ibn Ibrahimy Othman Omran Khalifa Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

This paper illustrates the classification of EMG signals through design and optimization of Artificial Neural Network (ANN). Different types of ANN models are basically structured with many interconnected network elements which can develop pattern classification strategies based on a set of input/training data. The ANN models work in parallel thus providing higher computational performance than traditional classifiers which function sequentially. The EMG signals obtained for different kinds of hand motions, which further denoised and processed to extract the features. Extracted time and time-frequency based feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The results show that the designed network is optimized for 10 hidden neurons with 7 input features and able to efficiently classify single channel EMG signals with an average success rate of 88.4%.

关键词： Electromyography Training Artificial neural networks Neurons Biological neural networks Feature extraction classification algorithms

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Automated Mitotic Nuclei classification Using Fruitfly Optimization Algorithm with Machine Learning on Breast Cancer Diagnosis

Automated Mitotic Nuclei Classification Using Fruitfly Optim...

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International Conference on Advances in Information Technology (ICAIT)

作者： I Gethzi Ahila Poornima Abdul Wasim Abdul Habib Sheikh Aruna Thota G. Devi Neha Pandey Sudipta Roy Department of CSE Ramco Institute Of Technology Rajapalayam OBGY Manwatkar College Of Nursing Chandrapur Department of IT Vignan Institute of Technology and Science RMK College of Engineering and Technology OBG Nursing Panna dhai maa subharti nursing college Meerut Uttar pradesh Pharmaceutics Bengal College of Pharmaceutical Technology Dubrajpur

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Mitotic nuclei classification plays an essential part in breast cancer analysis which provides an automatic and extremely exact technique to identify and classify mitotic figures in histopathological images. Mitotic figures are serious signs of cell propagation and a main norm in cancer classifying that makes their exact classification vital for defining severity and prediction of breast cancer. Leveraging advanced machine learning (ML) and computer vision models helps pathologists and oncologists by giving a trustworthy and effective device for mitotic nuclei recognition and classification. By importing speed and exactness of breast cancer diagnosis, it donates to initial involvement and personalized treatment plans, evenly enhancing patient results and complete efficacy of breast cancer management. This study presents an Automated Mitotic Nuclei classification using Fruitfly Optimization Algorithm by Machine Learning (AMNC-FFOAML) technique on Breast Cancer Diagnosis. The AMNC-FFOAML technique concentrates on automatic detection of the mitotic nuclei within histopathological images, the AMN C-FFOAML model applies a comprehensive methodology. It starts with bilateral filtering (BF) to eradicate the noise, assuring the quality of the images. In addition, the DenseNet model is used for capturing the complex features of the images. At last, classification is performed by 1-dimensional Convolutional Autoencoders (CAE) to precisely categorize mitotic nuclei. Furthermore, the model harnesses the FFO algorithm for hyperparameter tuning, optimizing its performance. The experimental results of the AMNC-FFOAML techniques were examined on a medical image dataset. The investigational values portray a considerable development of the AMNC-FFOAML technique in detection and classification of mitotic nuclei.

关键词： Machine learning algorithms Filtering Noise Machine learning Breast cancer classification algorithms Information technology

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classification of Massive User Load Characteristics in Distribution Network Based on Agglomerative Hierarchical Algorithm

Classification of Massive User Load Characteristics in Distr...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Yinglong Diao Ke-Yan Liu Lijuan Hu Dongli Jia Weijie Dong CEPRI China Electric Power Research Institute Beijing China

ISBN: (纸本)9781509051557

In order to improve primary energy utilization, achieve economical operation of distribution network, comprehensively consider the concentration / compensation needs of various groups under typical load levels, and to gain understanding of characteristics of different types of user loads, the present paper proposes a hierarchical cluster algorithm to enhance the cohesion of a distribution feeder load characteristic clustering algorithm circumstances. This will serve to ultimately provide effective guidance for electricity energy conservation as well as to better realize peak load shifting. By cutting distribution network load time sequence data in longitudinal manner, relevant feature were extracted to achieve user load characteristics classification based on hierarchical clustering algorithm. Such classification will therefore assist to optimize distribution network scheduling. It is therefore an effective way to enhance accuracy and effectiveness of relevant power distribution decision-making.

关键词： Clustering algorithms classification algorithms Data mining Feature extraction Timing Algorithm design and analysis Reactive power

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A new classification algorithm based on rough set and entropy

A new classification algorithm based on rough set and entrop...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jing Yang Hao Wang Xue-Gang Hu Zhong-Hui Hu Department of Computer Science and Technology Hefei University of Technology Hefei Anhui China

ISBN: (纸本)0780378652

A RSE algorithm for combining rough set theory and entropy heuristics is presented which can induce classification rules, which construction is based on information gain and equivalence relation. The algorithm applies to discrete-valued attributes. So the case of knowledge representation system with some discrete-valued condition attributes and one discrete-valued decision attribute is considered. Firstly, we select a condition attribute based on information gain; secondly, we use rough set theory to establish equivalence classes with respect to the selected condition attribute and decision attribute; finally, classification rules can be extracted from the equivalence classes. Furthermore, we can prove the RSE algorithm valid compared with ID3 algorithm.

关键词： classification algorithms Entropy Set theory Knowledge representation Machine learning algorithms Machine learning Decision trees classification tree analysis Data mining Computer science

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