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6 articles for “naïve Bayesian classification”
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Chi-Square Statistic and Principal Component Analysis Based Compressed Feature Selection Approach for Naïve Bayesian Classifier
Abstract: Many of the machine learning algorithms are based on an assumption of attribute independency and often used in domains where the assumption doesn’t hold true. Naïve Bayesian (NB) classifier makes assumption that all the features are conditionally independent given the class labels; In this paper, attribute dependencies were analyzed using Chi-Square test and the Principal Component Analysis (PCA) was carried out on the whole dataset to get a set of …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 2, Issue 2, 2015 · pp. 16–23 Read article
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Efficient Classification of Noisy Text
Abstract: Textual content comprises a significant volume of data generated online on a daily basis. The web-generated data often consists of high levels of noise due to a variety of factors. Development of efficient systems for automatic classification of noisy data is a crucial task in text mining. This paper examines a technique for classification of noisy text which is based on multiple feature selection and supervised learning. The main aim …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 56–61 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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Research Frame work of Missing Data Analysis using Mathematical Models
Abstract: AbstractThis article deals with providing an overall framework of missing data analysis using mathematical models. Mathematical models serves at large for missing data imputation. Several algorithms in machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier use Mathematical models for analysis. The performance of above mathematical models has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 7, Issue 3, 2020 · pp. 36–44 Read article
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Diabetic Level Prediction in Humans: Using Supervised Machine Learning Methods
Abstract: Diabetes could be an unremitting malady or a metabolic sick wellbeing cluster where an individual endures from a pervasive sum of insulin-producing blood glucose within the body or since the cells of the body do not react to affront. Steady diabetes hyperglycemia is the damage to long term organs, particularly lungs, kidneys, nerves, the heart and the veins are connected to weakness and misfortune. The objective of this investigation is …
Published in Recent Trends in Parallel Computing · Vol. 7, Issue 1, 2020 · pp. 1–6 Read article
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Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques
Abstract: Credit risk poses the most significant threat to financial and monetary institutions. Banks strive to offer loans that generate high returns while minimizing risk. Achieving this requires the ability to accurately identify and classify credit customers, both individuals and legal entities, according to their likelihood of fully meeting their obligations. This classification is done using relevant financial and non-financial criteria. The primary goal of this study is to assess the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 63–81 Read article