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检索条件"主题词=Classification Algorithms"

共 26309 条记录，以下是241-250 订阅

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Neural networks as a tool to generate pattern classification algorithms

Neural networks as a tool to generate pattern classification...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： S. Abe M. Kayama H. Takenaga T. Kitamura Hitachi Research Laboratory Hitachi and Limited Hitachi Japan

Extraction of algorithms from pattern classification neural networks is discussed. It is shown that input-to-hidden weights for three layers, or otherwise, hidden-to-hidden weights for four layers are determined so that corresponding hyperplanes separate all the training data in a class from the remaining classes. The extraction of a classification algorithm from a trained network is discussed, and its generalization ability is compared with that of the original network. Weights of the neural networks, generated by the backpropagation algorithm for number recognition, are tuned, and classification algorithms are demonstrated to be extracted.< >

关键词： Neural networks Pattern classification classification algorithms Neurons Training data Backpropagation algorithms Network synthesis Data mining Laboratories Multi-layer neural network

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Predicting Grade Promotion Using Decision Tree and Naïve Bayes classification algorithms

Predicting Grade Promotion Using Decision Tree and Naïve Ba...

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International Conference on Informatics and Computing (ICIC)

作者： Eni Irfiani Instianti Elyana Fintri Indriyani Frans Eduard Schaduw Danang Dwi Harmoko Information Systen Universitas Bina Sarana Informatika Jakarta Indonesia Office Administration Universitas Bina Sarana Informatika Jakarta Indonesia Information System Universitas Bina Sarana Informatika Jakarta Indonesia English Letters Universitas Bina Sarana Informatika Jakarta Indonesia

ISBN: (纸本)9781538669228

The rapid development of the digital era is now a major challenge for the education system in Indonesia, especially for high school students. Today's teenagers are in a digital age where the development and enhancement of knowledge in the field of digital technology has had a dramatic impact. Common problems faced by senior high school students include their social relationship, difficulty concentrating in learning, lack of discipline, disobedience to orderliness and disrespect toward teacher and friends. Of these problems have an impact that the student's value is less than the average so that students become class. For that reason, we need a solution to reduce the number of students who failing a grade. Data mining concerns the education world to improve the quality of education by minimizing the number of failing grade students. classification techniques on data mining used to process data that is Decision Tree and Naïve Bayes. Many data can be managed in schools using the method of data mining, therefore it is necessary to limit the problem so that the assessment can be focused and not floating. Limitations in the problem include Cognitive values, Psychomotor values, Affective values, Number of attendance Number of remedial exams. The results of the analysis will be used to predict the class increase that will affect the quality improvement of students in the future. Measurement results using confusion matrix and ROC curve obtained decision tree accuracy values of 97.5% and naïve bayes of 97.5%.

关键词： Decision trees Data mining classification algorithms Training Software algorithms Prediction algorithms

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Performance Analysis of Various Supervised Binary classification algorithms and their Optimized Variants on High-Dimension Limited-Sample-Size Data

Performance Analysis of Various Supervised Binary Classifica...

引用

IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Rohan Kumar Lala School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneshwar India

ISBN: (纸本)9781665409636

In modern times, high-dimension limited-sample-size (HDLSS) data have become a lot prevalent in data mining research areas. Performing analysis on HDLSS data is inherently difficult because of large number of features and low sample size. Dimensionality reduction is a recommended step before working on HDLSS data because of a phenomenon known as the ‘curse of dimensionality’. However in medical and psychiatric areas, these large number of features often prove to be extremely important in cases of life threatening disease detection or identifying biomarkers. The purpose of this study is to perform a comparative performance analysis of various supervised classification algorithms and their optimized variants on HDLSS data without any dimensionality reduction to recreate the aforementioned situations. Various hyper-parameter optimization techniques and AdaBoost meta-estimator classifier are implemented on these algorithms to find their optimized variants. Optimized Random Forest and CatBoost classifiers produced the most optimal results on the used HDLSS datasets. Relevant arguments and findings regarding the used eight different supervised classification algorithms, both base and optimized, are evaluated and summarized in this paper.

关键词： Dimensionality reduction Measurement Learning systems Predictive models classification algorithms Performance analysis Naive Bayes methods

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A comparative study of genetic sequence classification algorithms

A comparative study of genetic sequence classification algor...

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IEEE Workshop on Neural Networks for Signal Processing

作者： S. Mukhopadhyay Changhong Tang J. Huang Mulong Yu M. Palakal Department of Computer and Information Science Indiana University-Purdue University Indianapolis Indianapolis IN USA

classification of genetic sequence data available in public and private databases is an important problem in using, understanding, retrieving, filtering and correlating such large volumes of information. Although a significant amount of research effort is being spent internationally on this problem, very few studies exist that compare different classification approaches in terms of an objective and quantitative classification performance criterion. In this paper, we present experimental studies for classification of genetic sequences using both unsupervised and supervised approaches, focusing on both computational effort as well as a suitably defined classification performance measure. The results indicate that both unsupervised classification using the Maximin algorithm combined with FASTA sequence alignment algorithm and supervised classification using artificial neural network have good classification performance, with the unsupervised classification performs better and the supervised classification performs faster. A trade-off between the quality of classification and the computational efforts exists. The utilization of these classifiers for retrieval, filtering and correlation of genetic information as well as prediction of functions and structures will be logical future directions for further research.

关键词： Genetics classification algorithms Sequences Databases Clustering algorithms Artificial neural networks Information retrieval Information filtering Information filters Frequency

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An Analysis of classification algorithms for Cloud-Based Intrusion Detection Systems

An Analysis of Classification Algorithms for Cloud-Based Int...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Harry John Manjula Shenoy K Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Safeguarding computer networks from unauthorized access and malicious activities heavily relies on Intrusion Detection Systems (IDS), especially in cloud environments where data security is paramount. The effectiveness of an IDS depends heavily on the classification algorithms used to distinguish between legitimate and malicious network traffic. This paper aims to comprehensively study various classification algorithms used in IDS for intrusion detection within cloud environments. It compares the performance of various classification algorithms commonly employed in intrusion detection systems. Random Forest, Logistic Regression, Naive Bayes, Random Tree, support vector machines, and K-Nearest Neighbour are among the algorithms that have been reviewed. The algorithms have also been evaluated using key metrics, including accuracy, error rate, recall, and FValue. The findings provide valuable insights into the strengths and weaknesses of different classification algorithms, aiding in selecting and optimizing cloud-based IDS algorithms for network security. The proposed study employs two feature selection approaches on the NSL-KDD dataset to compare the various classification algorithms for intrusion detection. The results show that the Random Forest classifier with a feature selection of Principal Component Analysis achieves exceptional precision and minimal error, indicating its effectiveness for cloud-based intrusion detection.

关键词： Measurement Cloud computing Accuracy Error analysis Intrusion detection Telecommunication traffic Feature extraction classification algorithms Random forests Principal component analysis

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Enhancing Advanced Text classification algorithms for Topic Modelling in English Literature

Enhancing Advanced Text Classification Algorithms for Topic ...

引用

BIT Conference (BITCON), International

作者： G. Lakshmanan Gopal G. Raman Gopal Deepak Gupta K S Punithaasree Ajmeera Kiran M. Durairaj Sri Malolan College of Arts and Science Chengalpattu Tamil Nadu India Professor of English Tashkent Gumanitar Fanlar University Tashkent City Tashkent Uzbekistan Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of English Nandha Engineering College (Autonomous) Erode India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept.of English Panimalar Engineering College Chennai India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The research explores the enhancement of text classification and topic modeling in English literature through advanced transformer-based algorithms, specifically focusing on models like RoBERTa. Traditional methods such as "{Latent Dirichlet Allocation" (LDA) and "Latent Semantic Analysis" (LSA) have limitations in capturing the complexities of literary texts, which often feature layered meanings and diverse narrative structures. The study proposes leveraging transformers to improve classification accuracy and thematic coherence by utilizing self-attention mechanisms that analyze entire texts rather than relying solely on word frequency. By fine-tuning transformer models for literary analysis, this research aims to effectively discern complex themes and classify texts across various genres and historical contexts. The methodology includes the use of diverse datasets, including Wikipedia, Reuters, and 20-Newsgroups, to ensure robust training and testing of the models. Data preprocessing techniques such as tokenization, stemming, and lemmatization are employed to optimize the text for analysis. The anticipated outcome is a refined transformer-based tool that not only categorizes literature with greater accuracy but also aids scholars in uncovering thematic patterns, thereby enriching literary studies. The findings are expected to bridge the gap between machine learning techniques and the unique demands of literary analysis, ultimately enhancing scholarly research within the humanities. This advancement holds promise for transforming how literature is analyzed and interpreted in academic settings.

关键词： Training Analytical models Accuracy Text categorization Machine learning Coherence Transformers Tokenization classification algorithms Testing

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Assessment of different classification algorithms for burnt land discrimination

Assessment of different classification algorithms for burnt ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Olivier Zammit Xavier Descombes Josiane Zerubia Ariana research group INRIA/I3S Sophia-Antipolis France

In this paper, satellite-based remote sensing techniques are used for assessing the damage after a forest fire. Here, burnt land mapping is based on a single after-fire satellite image (SPOT 5). Both support vector machines (SVM) and traditional classification algorithms such as the K-nearest neighbours or the K-means are used to discriminate burnt from unburnt areas. An automatic method combining K-means and SVM is presented and its performances are compared to more classical methods. Maps produced by the different classifiers are also compared to official ground truth provided by the French Space Agency (CNES).

关键词： classification algorithms Support vector machines Support vector machine classification Fires Remote sensing Satellites Supervised learning Image classification Constraint optimization Ecosystems

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Household Waste classification algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization

Household Waste Classification Algorithms Based on Various C...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Huang Renxiang Chen Jia Li Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research.

关键词： Deep learning Analytical models Technological innovation Attention mechanisms Reviews Data augmentation Data models classification algorithms Convolutional neural networks Optimization

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Optimization of circuit partitioning in VLSI through classification algorithms

Optimization of circuit partitioning in VLSI through classif...

引用

IEEE Region 10 International Conference TENCON

作者： K. A. Sumithradevi M. N. Vijayalakshmi Department of MCA R.V. College of Engineering Bangalore India

In order to build complex digital logic circuits it is often essential to sub-divide multi million transistors design into manageable pieces. Circuit partitioning in VLSI, is one of the major area of research. There are many existing diverse algorithm to partition the circuit into sub circuits. This paper aims at circuit partitioning using two classification algorithms Decision Tree Algorithm and K-Nearest Neighbors Algorithm. These two algorithms were tested on a 3-bit Priority Encoder and a 4×2 SRAM sample circuits and implemented using VHDL. The tested result shows that the K-Nearest Neighbor algorithm yields better subcircuits than the Decision Tree Algorithm.

关键词： classification algorithms Partitioning algorithms Algorithm design and analysis Integrated circuit interconnections Random access memory classification tree analysis

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Comparative analysis of classification algorithms for hyperthyroidism

Comparative analysis of Classification algorithms for hypert...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P. Lakshmi D. Ramyachitra Bharathiar University Coimbatore India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

The most diagnosed disease prediction among worldwide, expression of biological data plays a vital role. It performs the critical task and a take apart to the hypothyroidism disease prediction. Interpreting the information from the biological data is an active area in bioinformatics research and it remains a complicated problem, due to the high dimensional and low sample size. This paper focuses on classifying the dataset to detect hypothyroidism. Hence, the classification is done by using cross-validation on four classification algorithms such as Naïve Bayes, SMO (Sequential Minimum Optimization algorithm), Ada Boost, and Random Forest. At last, the comparative analysis is carried out by using the performance measures. From the experiment result, it is inferred that the Random Forest algorithm provides better results out of the existing methods.

关键词： classification algorithms Forestry Biochemistry Diseases Area measurement Weight measurement Optimization

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