Results 291 to 300 of about 102,648 (335)

Efficient Jamming Identification in Wireless Communication: Using Small Sample Data Driven Naive Bayes Classifier

IEEE Wireless Communications Letters, 2021
The identification of malicious jamming pattern is significant for wireless communication system to adopt targeted anti-jamming approaches. Efficient identification of malicious jamming patterns requires sufficient sample data and computing resources for
Yuxin Shi, Xinjin Lu, Yingtao Niu, Yusheng Li   +3 more
semanticscholar   +1 more source

Learning an Optimal Naive Bayes Classifier

18th International Conference on Pattern Recognition (ICPR'06), 2006
The naive Bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. However, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes.
Miriam Martinez-Arroyo, Luis Enrique Sucar   +1 more
openaire   +1 more source

LNBC: A Link-Based Naive Bayes Classifier

2009 IEEE International Conference on Data Mining Workshops, 2009
Many databases store data in relational format, with different types of entities and information about links between the entities. Link-based classification is the problem of predicting the class label of a target entity given information about features of the entity and about features of the related entities.
Bahareh Bina, Oliver Schulte, Hassan Khosravi   +2 more
openaire   +3 more sources

Multi-Feature Analysis for Automated Brain Stroke Classification Using Weighted Gaussian Naïve Bayes Classifier

J. Circuits Syst. Comput., 2020
In today’s world, brain stroke is considered as a life-threatening disease provoked by undesirable blockage among the arteries feeding the human brain.
S. Jayachitra, A. Prasanth
semanticscholar   +1 more source

A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews

2020 International Conference on Contemporary Computing and Applications (IC3A), 2020
This paper represents a comparison between two machine learning approaches for analyzing the sentiment of the customers’ reviews on Amazon products. Eventually, reviews of a product help the customers to understand the product quality.
Sanjay Dey, Sarhan Wasif, Dhiman Sikder Tonmoy, Subrina Sultana, Jayjeet Sarkar, Monisha Dey   +5 more
semanticscholar   +1 more source

Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

, 2020
The largest organ and the outer covering of the human body is the skin. With seven layers of it covering the other organs inside, skin is one of the important part to take care of.
V. R. Balaji, S. Suganthi, R. Rajadevi, V. K. Kumar, Saravana Balaji B, S. Pandiyan   +5 more
semanticscholar   +1 more source

Generalized Naive Bayes Classifiers

ACM SIGKDD Explorations Newsletter, 2005
This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and predictive analysis.
openaire   +1 more source

Bayes’ Theorem and Naive Bayes Classifier

This article gives a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from the fundamental elements of probability theory, this article outlines all steps leading to one of
Daniel Berrár
exaly   +1 more source

Text filtering by boosting naive Bayes classifiers

Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000
Several machine learning algorithms have recently been used for text categorization and filtering. In particular, boosting methods such as AdaBoost have shown good performance applied to real text data. However, most of existing boosting algorithms are based on classifiers that use binary-valued features.
Yu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang   +2 more
openaire   +1 more source

Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

Data & Knowledge Engineering, 2020
Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data.
C. Banchhor, N. Srinivasu
semanticscholar   +1 more source