AN L1-REGULARIZED NAÏVE BAYES-INSPIRED CLASSIFIER FOR DISCARDING REDUNDANT AND IRRELEVANT PREDICTORS

2013 ◽  
Vol 22 (04) ◽  
pp. 1350019 ◽  
Author(s):  
DIEGO VIDAURRE ◽  
CONCHA BIELZA ◽  
PEDRO LARRAÑAGA
Keyword(s):  
Supervised Classification ◽  
Naive Bayes ◽  
Harmful Effect ◽  
Real Data ◽  
Naïve Bayes ◽  
Computationally Efficient ◽  
Data Set ◽  
Regularization Technique ◽  
Bayes Model ◽  
Wide Range

The naïve Bayes model is a simple but often satisfactory supervised classification method. The original naïve Bayes scheme, does, however, have a serious weakness, namely, the harmful effect of redundant predictors. In this paper, we study how to apply a regularization technique to learn a computationally efficient classifier that is inspired by naïve Bayes. The proposed formulation, combined with an L1-penalty, is capable of discarding harmful, redundant predictors. A modification of the LARS algorithm is devised to solve this problem. We tackle both real-valued and discrete predictors, assuring that our method is applicable to a wide range of data. In the experimental section, we empirically study the effect of redundant and irrelevant predictors. We also test the method on a high dimensional data set from the neuroscience field, where there are many more predictors than data cases. Finally, we run the method on a real data set than combines categorical with numeric predictors. Our approach is compared with several naïve Bayes variants and other classification algorithms (SVM and kNN), and is shown to be competitive.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

scholarly journals Mining the crime data using naïve Bayes model

2021 ◽  
Vol 23 (2) ◽  
pp. 1084
Author(s):  
Lourdes M. Padirayon ◽  
Melvin S. Atayan ◽  
Jose Sherief Panelo ◽  
Carlito R. Fagela, Jr
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Police Departments ◽  
Data Set ◽  
Crime Data ◽  
Bayes Model ◽  
Massive Number ◽  
Index Crime

<p>A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the complexity of processing voluminous crime data. Approximately 439 crimes have been registered in sanchez mira municipality in the past seven years. Police officers have no clear view as to the pattern crimes in the municipality, peak hours, months of the commission and the location where the crimes are concentrated. The naïve Bayes modelis a classification algorithm using the Rapid miner auto model which is used and analyze the crime data set. This approach helps to recognize crime trends and of which, most of the crimes committed were a violation of special penal laws. The month of May has the highest for index and non-index crimes and Tuesday as for the day of crimes. Hotspots were barangay centro 1 for non-index crimes and barangay centro 2 for index crimes. Most non-index crimes committed were violations of special law and for index crime rape recorded the highest crime and usually occurs at 2 o’clock in the afternoon. The crime outcome takes various decisions to maximize the efficacy of crime solutions.</p>

Machine Learning increases efficiency and timeliness of National Procedural Clinical Standards KPI reporting (Preprint)

2021 ◽  
Author(s):  
Graeme Hart ◽  
Michael Woodburn ◽  
Nada Marhoon ◽  
Alan Pritchard ◽  
Jeff Feldman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Free Text ◽  
Data Set ◽  
Quality Reporting ◽  
Bayes Model ◽  
Data Pipeline ◽  
Naïve Bayes Model

BACKGROUND Background: Quality Assurance activities are frequently dependent on manual assessment of text-based records. Increasingly, these records have digital structures that may be amenable to computer analysis. We used the Australian Commission for Safety and Quality in Healthcare (ACSQHC) National Clinical Care Colonoscopy standard reporting requirement as a proof of concept for an analytics process to streamline and reduce manual reporting overheads. The endoscopy unit performs approximately 4,500 colonoscopies (mainly outpatient) per year. Quarterly reporting of colonoscopy outcomes requires approximately 30 hours of manual data abstraction, collation and combination from a variety of electronic databases. The most time consuming is manual retrieval and abstraction of histopathology records from the EMR. OBJECTIVE 1. To reduce the manual overheads of quarterly National Standards KPI reporting for colonoscopy compliance using an automated data pipeline and Artificial Intelligence tools. 2. The service also wished to minimise the risk of failure to follow up in new cancer diagnoses for outpatient colonoscopies. 3. To develop a data and analytic pipeline that would be easily re-purposed for additional standards, audit and research projects. METHODS A data pipeline and analysis environment were established in the hospitals’ secure Microsoft Azure databricks resource. A Training data set of 1000 colonoscopies was extracted using from the procedural Provation database using the the ProvationMD ® reporting tool and linked to relevant histopathology reports provided from the Clinical Research Data Warehouse (CRDW). The Machine Learning (ML) training data set was created when histopathological reports were manually coded by Gastroenterology Registrars & nurses into the following categories: Adenoma Clinically Significant Sessile Serrated Adenoma Cancer Adequate Bowel Preparation Complete examination A variety of Natural Language Processing (NLP) & ML models were assessed and refined to minimize error rate. Sensitivity was prioritised for the diagnosis of Cancer to minimize missed cases. Reporting to clinicians and quality co-ordinators was established using Microsoft Power BI. RESULTS The Naïve Bayes model for multinomial data resulted in high accuracy, but impacted recall. Sensitivity improved using a virtual ensemble approach, layering models within the processing pipeline and maximised using Microsoft’s ® Text Analytics – Healthcare NLP model with our custom Naïve Bayes model. F1 scores between 0.89 and 0.93 were achieved. The algorithm checks daily for new data and performs the analysis. Quarterly analysis and reporting time decreased from 30 hours to less than 5 minutes and reports can now be continuously updated in the Microsoft Power BI reporting portal. CONCLUSIONS Advanced analytic techniques can be deployed for mandatory quality reporting in a secure, cloud based, hospital data domain. The cost was far less than the manual processes it replaces. Reporting is more timely as it is automated. The potential for training such algorithms for other QA reporting is high. Text based research and audit within the free text domain of the EMR clinical documentation also becomes possible. CLINICALTRIAL Not applicable

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language

2021 ◽  
Vol 30 (1) ◽  
pp. 774-792
Author(s):  
Mazin Abed Mohammed ◽  
Dheyaa Ahmed Ibrahim ◽  
Akbal Omran Salman
Keyword(s):  
Natural Language ◽  
Naive Bayes ◽  
False Negative ◽  
Naïve Bayes ◽  
Final Decision ◽  
Learning Approach ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Wide Range

Abstract Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.

scholarly journals Session Segmentation Method Based on Naïve Bayes Model

2012 ◽  
Vol 6-7 ◽  
pp. 576-582
Author(s):  
Ping Li ◽  
Ming Liang Cui ◽  
Zhen Shan Hou ◽  
Liu Liu Wei ◽  
Wen Hao Ying ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Time Interval ◽  
Segmentation Method ◽  
Retrieval Process ◽  
Search Activity ◽  
Query Suggestion ◽  
Bayes Model ◽  
Discrimination Model ◽  
Naïve Bayes Model

Session segmentation can not only contribute a lot to the further and deeper analysis of user’s search behavior but also act as the foundation of other retrieval process researches based on users’ complicated search behaviors. This paper proposes a session boundary discrimination model utilizing time interval and query likelihood on the basis of Naive Bayes Model. Compared with previous study, the model proposed in this paper shows a prominent improvement through experiment in three aspects, which is: recall ratio, precision ratio and value F. Owing to its advantage in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which is related to search results improvement.

Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications

2020 ◽  
Vol 15 (2) ◽  
pp. 136-143
Author(s):  
Omid Akbarzadeh ◽  
Mohammad R. Khosravi ◽  
Mehdi Shadloo-Jahromi
Keyword(s):  
Integral Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Experimental Comparison ◽  
Classifier Combination ◽  
Fuzzy Integral ◽  
Combination Rules ◽  
Classifier Design ◽  
Combination Of Classifiers ◽  
Wide Range

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusion: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

scholarly journals An Intrusion Detection Model based on Hybrid Classification algorithm

2018 ◽  
Vol 246 ◽  
pp. 03027
Author(s):  
Manfu Ma ◽  
Wei Deng ◽  
Hongtong Liu ◽  
Xinmiao Yun
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Performance Requirements ◽  
Hybrid Classification ◽  
Bayes Algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

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