Naive Bayes Regression Research Articles

Predicting compressive strength of ultra-high-performance concrete using Naive Bayes regression in novel approaches

Predicting compressive strength of ultra-high-performance concrete using Naive Bayes regression in novel approaches

Integrates Differential Gene Expression analysis and deep learning for accurate and robust prostate cancer diagnosis

The challenge of diagnosing complex diseases and increasing human lifespan is a pressing task. Traditional methods, relying on visual characteristics like ultrasound and angiography, often struggle to detect cancer in its early stages, limiting diagnostic accuracy due to the intricate and nonlinear nature of diseases. From the perspective of gene expression, detecting cancer offers a more robust and effective approach due to its ability to directly assess the genetic activity within cells. In this study, we present the development of a prostate cancer feature selection method based on differentially expressed genes (DEGs). Utilizing datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we meticulously curated data for both model training and testing, implementing stringent filtering criteria based on p-value and fold change. Our study identifies a panel of 220 genes with substantial potential for prostate cancer detection. We then construct an ANN model for the diagnosis of the disease, whose accuracy is 0.780.01, which is more effective than other models like Ridge Classifiers, Logistic Regression, Naive Bayes Regression and Decision Trees. The average accuracy of these classifiers is 0.730.01. Notably, these genes also demonstrate exceptional performance across other various classifiers, indicating their robustness and effectiveness without dependence on specific models. The credibility is validated by comparison to random genes, and adaptability by using pancreatic cancer data from GEO. The Gene Otology analysis also verifies the feasibility of such method. This panel establishes a solid foundation for advancing clinical diagnostics of prostate cancer. This framework holds potential to significantly transform prostate cancer screening by offering strong resilience and precision across multiple classification methods.

Penerapan Algoritma Naive Bayes dan Multiple Linear Regression Untuk Prediksi Status dan Plafon Kredit (Studi Kasus: Bank ABC)

Along with changing technology, Human resources are still needed in many parts of decision making. The companies and organizations still use human to analysis data. Despite of that performance, human analysis took longer time and effort to complete. And sometimes there is always a negative factor of human resources such as unmanageable human. Therefore, it’s always important to provide an excellent training source so this human resource able to reach an outcome that needed. Machine learning is one of the most common knowledge that use in decision making. There are many forms of machine learning such as regression, classification, clustering, etc. two of which is used in this application, regression and classification. Naive Bayes regression is one of classification method which rooted on Bayes theorem. Naïve Bayes use historical data to predict future outcome based on the characteristic on that historical data. Multiple Linear Regression involves more than one independent variable or predictor. With machine learning and human resources, man can easily to analyse credit worthiness and determine the credit limit of one bank costumer without taking a long time and much effort.

Depression prognosis using natural language processing and machine learning from social media status

<p><span>Depression is an acute problem throughout the world. Due to worst and prolong depression many people dies in every year. The problem is that most of the people are not concern of the fact that they are suffering from depression. In this research, our aim was to find out whether an individual is depressed or not by analyzing social media status. Therefore, we focused on real data. Our dataset consists of 2000 sentences, which was collected from different social media platforms Facebook, Twitter, and Instagram. Then, we have performed five data pre-processing approaches for natural language processing (NLP) such as tokenization, removal of stop words, removing empty string, removing punctuations, stemming and lemmatization. For our selected model, we considered that processed data as an input. Finally, we applied six machine learning (ML) classifiers multinomial Naive Bayes (NB), logistic regression, liner support vector classifier, random forest, K-nearest neighbour, and decision tree to achieve better accuracy over our dataset. Among six algorithms, multinomial NB and logistic regression performed well on our dataset and obtained 98% accuracy.</span></p>

Parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm and its realisation on Hadoop for big data

Collaborative filtering (CF) algorithms are widely used in a lot of recommender systems. However, space-time overhead and high computational complexity hinder their use in large-scale systems. This paper implements the parallel naive Bayes regression model based collaborative filtering recommendation algorithm on Hadoop computing platform to scalability problem of CF. Firstly, this paper analysis the inherent parallelism of the naive Bayesian regression model and constructs the theoretical model of naive Bayesian parallelisation. Secondly, the parallel naive Bayes regression model-based collaborative filtering recommendation algorithm is realised on Hadoop platform with distributed Hadoop distributed file system (HDFS) and MapReduce as the transparent distributed infrastructure. And its temporal-spatial overhead, speedup is discussed. Finally, applying parallel the naive Bayes regression model-based collaborative filtering recommendation algorithm to a large dataset. The experiment results on Netflix dataset show that this method has high scalability and less space-time overhead, which is suitable for real-time recommendation on large dataset.

Statistical modeling of tree failures during storms

The failure of trees during storms imposes strong economic and societal costs. Statistical modeling for predicting the probability of a tree failing during storms has the potential to help improve tree risk management. The purpose of this study is to explore the potential predictability of tree failure using advanced predictive modeling approach. These models also have broader applicability for modeling failures of technical systems during adverse weather events. To train and test models, we use a data set from a real case study in Massachusetts, USA. We compare the out-of-sample predictive accuracy of several machine learning models including logistic regression, classification and regression trees, multivariate adaptive regression splines, artificial neural network, naive-Bayes regression, random forest, boosting, and an ensemble model of boosting and random forest. Our results demonstrate that the ensemble model of boosting and random forest achieves the best prediction accuracy in predicting the failure probability of trees for the case study storm. Our results can help tree care professionals make better decisions to reduce the risk of tree failure prior to the storm.

Naïve Bayes regression model and its application in collaborative filtering recommendation algorithm

Aiming at the problem that the general Bayes classification model cannot be applied to regression prediction, a Naive Bayes regression model is proposed. Firstly, in order to simplify the complexity of model, attribute values and decision values are discretised from continuous value to discrete value. By summing the probability of Bayesian classification and using the mathematical expectation value as the regression value, the process of regression problem is turned into a classification problem and the Naive Bayes classification model was modified into Bayesian regression model. The difference of input attribute values, output values, application scope, requirement of output values, operation to obtain output values between Naive Bayes classification model and Naive Bayes regression model is compared in detailed. Secondly, the Naive Bayes regression model is application in collaborative filtering recommendation. This study identifies the user and the item as independent attribute characteristics, while rating as the classification category. And the attribute values and category values are discretised to simplify the complexity of Naive Bayes regression model. The experiment results on Movielens-100k, Eachmovie and Jester data set show that this new method has high success rate and efficiency.