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Boosting the Discriminant Power of Naive Bayes 26

Boosting the Discriminant Power of Naive Bayes

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wang, Shihe Ren, Jianfeng Lian, Xiaoyu Bai, Ruibin Jiang, Xudong Univ Nottingham Sch Comp Sci Ningbo Peoples R China Ningbo Beilun Taihe High Sch Ningbo Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. the proposed stack auto-encoder consists of two auto-encoders for different purposes. the first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. the second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. the proposed method is evaluated on a set of machine-learning benchmark datasets. the experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.

关键词： Correlation Codes Benchmark testing Feature extraction Boosting data models Numerical models

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Intonation Analysis and Classification of Declarative Questions and Statements in Nepali Language using machine learning Approaches 4th

Intonation Analysis and Classification of Declarative Questi...

引用

4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Subba, Ranjit Prasad, Roshan Kumar Rai, Pratika Sinha, Sharad Department of Computer Science Hijli College Kharagpur West Bengal Kharagpur721306 India Department of Computer Science and Technology University of North Bengal Raja Rammohunpur West Bengal Darjeeling734013 India

ISBN: (纸本)9789819757022

A speaker’s perspective cannot be fully expressed by phoneme segment in a speech, but it also comes along with the intonation. In the Nepali language, declarative questions are uttered with rising intonation and are commonly used in daily conversation. In this paper, training on various machine learning models has been performed to classify declarative sentences and questions. the dataset consists of 700 utterances with ~ 300 declarative statements (falling intonation) and ~ 400 declarative question statements (rising intonation) which are either recorded in the studio or taken from the "Large Nepali ASR training dataset" from ***. the dataset was pre-processed and annotated using PRAAT. Cepstral features MFCCs of every utterance in the data set were calculated and put to use in training five different supervised machine learning models. the data set is split into two parts: 70% for training and 30% for testing purposes, respectively. the comparative study shows that the Random Forest model performed better with the highest evaluation score of ~ 0.87% and the Naïve Bayes model achieved a minimal accuracy of approximately 0.69%. these findings have the potential to enhance the authenticity of Text-to-Speech, Automatic–Speech-recognition, and Speech-to-Text systems in languages with limited linguistic resources, like Nepali. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision trees

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Fashion Recommendation System Using machine learning 4

Fashion Recommendation System Using Machine Learning

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sivaranjani, Lingala Rachamadugu, Sandeep Kumar Reddy, B.V. Suresh Basi Reddy, A. Sakthivel, M. Depuru, Sivakumar School of Computing Mohan Babu University Tirupati India G. Pulla Reddy Engineering College Dep. of Cse A.P Kurnool India Koneru Lakshmaiah Education Foundation Dept. Of. Cse A.P Vaddeswaram India Tirupati India

ISBN: (纸本)9798350300888

For the past few years, fast fashion has become very popular, which has had a great impact on the textile and fashion industries. Fashion is an integral part of one's daily lives, and it has a significant impact on identity and self-expression. With the increasing availability of digital platforms and e-commerce websites, the fashion industry has transformed, and the way one shops for clothing has evolved. the rise of e-commerce has brought about a rise in the use of fashion recommendation systems. these systems aim to offer personalized product recommendations to users according to their interests and preferences. the field of machine learning has made significant strides in the development of image processing techniques, parsing techniques, image classification, segmentation, and networking, making it an ideal candidate for powering these recommendation systems. With these advancements in technology, the potential for fashion recommendation systems to provide even more accurate and individualized product recommendations is substantial. In this study, the dataset is used and the main technique is to use machine learning by splitting into training and testing data of the dataset. the convolutional neural network is used to produce similar items in the recommendation system. the CNN layers are upgraded by using RESNET50 and the filtering content is based on data provided for the product. the RESNET50 helps in overcome the problem of vanishing gradient. then KNN algorithm is used to recommend similar items. the main idea behind KNN is Euclidean distance and Cosine Similarity which helps in producing similar products. the user's past behavior is significant here and the convolutional neural networks (CNN) model was also utilized for picture categorization and recognition. the experimental results of the system achieved retrieval accuracy and outperformed the baseline. © 2023 IEEE.

关键词： Image recognition

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Research on Comprehensive Identification and Classification Technology for Latent Faults in High- Voltage Switchgear Based on machine learning 4

Research on Comprehensive Identification and Classification ...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Tang, Bin Liang, Qinqin Han, Fangyuan Yu, Min Zhang, Longfei Luo, Zongchang Luo, Chuansheng Hu, Mengzhu Wang, Jialin Zhu, Liping Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd Nanning China Guangxi Power Grid Co. Ltd Guangxi China

ISBN: (纸本)9798350308082

With the development of ultra-high voltage projects in China, the scale of power grids has been expanding. High-voltage switchgear is widely utilized in various substations and the power industry, gradually becoming a critical component of the power system. In response to the issue of latent faults in high-voltage switchgear, this paper proposes a pulse current detection method based on machine learning to achieve detection and classification of latent faults in high-voltage switchgear. the Hilbert-Huang transform is employed along with the theory of energy entropy to extract discharge characteristic parameters, which serve as input vectors for the pattern recognition network. the discharge types are recognized using a Probabilistic Neural Network (PNN), and optimization algorithms are applied to enhance the performance of the PNN. the recognition accuracy before and after optimization is compared. the results indicate that although this method requires a longer recognition time, the recognition accuracy is relatively high, reaching 98.7%. this approach plays a significant role in achieving comprehensive identification and classification of latent faults in high-voltage switchgear. © 2023 IEEE.

关键词： Hilbert-Huang transform

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Course Material Recommendation System Using Student learning Behavior and Course Material Complexity Score for Slow Learner Students 4th

Course Material Recommendation System Using Student Learning...

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4th international conference on data, Engineering, and Applications, IDEA 2022

作者： Bunkar, Kamal Arya, Chhaya Tanwani, Sanjay Kumar Institute of Computer Science Vikram University Ujjain India Pt. JNIBM Vikram University Ujjain India SCS&IT DAVV Indore India

ISBN: (纸本)9789819700363

Educational data mining (EDM) is not only a process of applying data mining algorithms on academic data. that is a process of exploring and providing solutions at various levels of educational system. that is useful for students, educators, management, and administration for decision-making and preparing futuristic strategies. the proposed EDM framework is motivated to enhance the student academic performance. the focus is on threefold: first to identify the student learning behavior to support the students, and teacher provide the remedial actions on the weak students. We propose clustering method for the study of students’ learning behavior associated with positive and negative outcomes (in exams) by utilizing data mining techniques. Second, there is some classifying approach to characterize student based on performance measure that they earn in examination. Applying supervised classifier on the datasets, we have found significant improvement in results. Finally, the study turns toward developing a technique for recommending appropriate study material by calculating readiness of student and complexity of course material. To achieve the objectives, three models are proposed and combined into one for designing accurate and efficient course material recommendation model. In initial steps, popular data mining algorithms, i.e., K-Means, fuzzy c-means (FCM), and kernel-based FCM (K-FCM), are implemented to cluster students according to their learning behaviors. the comparative study demonstrates that K-FCM-based pattern identification is providing more accuracy with respect to other two algorithms. On the other hand to design a model for student performance prediction, two supervised classifiers, i.e., C4.5 and CART, are implemented. During experiments, we found that the C4.5 decision works well for student performance dataset and for predicting the performance. Using both the components, a course study material recommendation model is proposed. thus an application is i

关键词： Students

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Association Rules Based Feature Extraction for Deep learning Classification 4th

Association Rules Based Feature Extraction for Deep Learning...

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4th international conference on Soft Computing and its Engineering Applications (icSoftComp)

作者： Kharsa, Ruba Al Aghbari, Zaher Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

ISBN: (纸本)9783031276088;9783031276095

the number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machine learning classifier. therefore, in this paper, we proposed the use of association rules to select features from medical data, which result in dimensionality reduction of the input feature space. the selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. the conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machine learning models in terms of accuracy of classification.

关键词： Association rules mining Feature selection Deep learning classification Resnet Neural networks Classification

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Proceedings of the 4th African Human Computer Interaction conference, AfriCHI 2023

Proceedings of the 4th African Human Computer Interaction Co...

引用

4th African Human Computer Interaction conference, AfriCHI 2023

ISBN: (纸本)9798400708879

the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physical workers;an interface design methodology for serving machine learning models;lions out of bounds? reflections on digital technology and matristic design to address human-wildlife conflict;an integration model to enhance information systems administration and data sharing – a case of Namibian lower courts;unmasking trust: examining users’ perspectives of facial recognition systems in Mozambique;and investigating the efficacy of large language models in reflective assessment methods through chain of thoughts prompting.

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Emotion Detection from Textual data Using Supervised machine learning Models 4

Emotion Detection from Textual Data Using Supervised Machine...

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4th international conference for Emerging Technology, INCET 2023

作者： Malagi, Rakshit R Yogith, R. Sai Prashanth, T.K. Kodipalli, Ashwini Rao, Trupthi Rohini, B.R. Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350335750

Emotion detection and recognition from text is a recent field of research that is closely related to Sentiment analysis. Many people express themselves using text, photographs, music, and video. Text communication using web-based networking platforms, however, could be a little overwhelming. Every second, a substantial amount of unstructured data is produced on the Internet as a result of social media sites. this is where sentiment analysis, which recognises polarity in texts, can be useful. It assesses the author's attitude towards a specific object, administration, person, or location and concludes if it is positive, negative, or neutral. In some cases, sentiment analysis is inadequate, necessitating emotion detection, which precisely ascertains a person's mental/emotional state. the development of a text-based emotion detection and prediction model is the primary goal of this work. the development of emotion analysis is confronting several market hurdles, with accuracy being the key one. As a result, the Decision Trees, Naive Bayes, Support Vector machine, Logistic Regression, k-Nearest Neighbors and Random Forest, supervised machine learning classification algorithms were examined. the six main emotions recognized by Ekman are joy, fear, anger, love, surprise, and sadness, these were the foundation through which the model was constructed. strategies for preprocessing data containing stemming, stop-words, numerals, and punctuation marks removal, tokenization, and spelling correction were implemented. this review paper delves into the degrees of several emotion models as well as the technique of emotion detection from text. © 2023 IEEE.

关键词： Decision trees

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Accuracy Improvement Based on Classic Neural Network: Voting, Restarting and Quantization 4

Accuracy Improvement Based on Classic Neural Network: Voting...

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4th international conference on Computing and data Science, CONF-CDS 2022

作者： Zhu, Yunxiang Zhao, Yanpeng Liu, Guanghong Zeng, Lifan School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai200444 China University of Wisconsin-Madison WI53711 United States School of Electrical Engineering Zhejiang University Zhejiang310027 China Electrical Engineering of the State University of Buffalo buffaloNY14260 United States

Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people's research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. the part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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machine learning Solutions for Predicting Bankruptcy in Indian Firms 27th

Machine Learning Solutions for Predicting Bankruptcy in In...

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27th international conference on pattern recognition, ICPR 2024

作者： Chaithra Sharma, Priyanshu Mohan, Biju R. National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031781216

the growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determining the worthiness of an investment. the Indian stock market offers investment opportunities, but it also involves risk. As a result, it is critical to invest in fundamentally sound companies for long-term investment. To address this need, we created a machine learning-based model for identifying a healthy and distressed firm in the Indian scenario. We created a dataset consisting of 118 bankrupt and 310 healthy firms. the dataset contains three labels: bankrupt, healthy, and financial distress. the addition of the financial distress category improves our ability to recognize and identify firms that are more likely to declare bankruptcy. Recognizing the shortcomings of limited data in the Indian scenario in previous research, our study aimed to include more data instances for training. the dataset included widely recognized financial ratios and macroeconomic data that recognize the interconnectedness of broader economic trends with the company’s financial health. Advanced machine learning algorithms, namely Support Vector machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM), Categorical Boosting (CatBoost), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were applied. the XGBoost and LGBM demonstrated the highest level of classification accuracy and also performed well on real-world data, demonstrating their potential use in supporting investors with decision-making processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision making

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