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.

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 A Bayesian approach to optimizing stem cell cryopreservation protocols

2014 ◽  
Author(s):  
Sammy Sambu
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Embryonic Stem ◽  
Cell Types ◽  
Naïve Bayes ◽  
3D Scaffold ◽  
Decision Tree Learning ◽  
Cross Sectional ◽  
Meta Data ◽  
Wide Range

Cryopreservation is beset with the challenge of protocol alignment across a wide range of cell types and process variables. By taking a cross-sectional assessment of previously published cryopreservation data (sample means and standard errors) as preliminary meta-data, a decision tree learning analysis (DTLA) was performed to develop an understanding of target survival based on different approaches. Briefly, using a DTLA approach, a clear direction on the decision process for selection of methods was developed with key choices being the cooling rate, plunge temperature on the one hand and biomaterial choice, use of composites (sugars and proteins), loading procedure and cell location in 3D scaffold. Since machine learning and generalized approaches were employed, these metadata could be used to develop posterior probabilities via Naïve Bayes Classification (NBC) for combinatorial approaches that were not initially captured in the metadata. These results showed that newer protocol choices could lead to improved cell survival consistent with physical reports. In conclusion, this article proposes the use of DTLA models and NBC for the improvement of modern cryopreservation techniques through an integrative approach.Keywords: 3D cryopreservation, decision-tree learning (DTL), sugars, mouse embryonic stem cells, meta-data, Naïve Bayes Classifier (NBC)

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 Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis

2021 ◽  
Vol 6 (1) ◽  
pp. 33
Author(s):  
Reiza Adi Cahya ◽  
Fitra A. Bachtiar ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Naive Bayes ◽  
Service Providers ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
Support Vector ◽  
Combination Rule ◽  
Combination Rules ◽  
Softmax Regression ◽  
Ensemble Technique ◽  
Computational Resources

The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.

scholarly journals Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
N. Pérez-Díaz ◽  
D. Ruano-Ordás ◽  
F. Fdez-Riverola ◽  
J. R. Méndez
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Wide Range ◽  
Vector Machines ◽  
Bayes Algorithm

Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

scholarly journals Data Mining using Naive Bayes classifier: an application in short news

2017 ◽  
Author(s):  
Thais Neubauer ◽  
Sarajane Peres
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Information Age ◽  
Naïve Bayes ◽  
Text Representation ◽  
Bayes Classifier ◽  
Interesting Application ◽  
Satisfactory Performance ◽  
Wide Range ◽  
Classification Tasks

In the information age, a plethora of content is available on a wide range of subjects, requiring an organization capable of making that content more accessible and engaging. An interesting application of classification tasks was identified in the Index project, developed by the Amsterdam-based company The Next Web. To solve this classification task, the Naive Bayes (NB) technique was applied to classify short news in four topics. To evaluate the results produced by such a classifier, a series of tests using cross-validation were carried out. It was possible to conclude that the NB classifier had satisfactory performance, achieving about 70% of accuracy in the best cases. In this paper, we intend to present the context of the Index project and discuss the results obtained with the NB classifiers. Despite the good results, the project is still in progress, as it is necessary to test variations as classification techniques and text representation approaches.

scholarly journals IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

2020 ◽  
Vol 3 (2) ◽  
pp. 110-117
Author(s):  
Irayori Loelianto ◽  
Moh. Sofyan S Thayf ◽  
Husni Angriani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Design Stage ◽  
Prospective Students ◽  
Classifier Design ◽  
Python Programming Language ◽  
Bayes Theory ◽  
Python Programming

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.

scholarly journals Disambiguierung deutschsprachiger Diskursmarker: Eine Pilot-Studie

2004 ◽  
Vol 18 (1) ◽  
Author(s):  
Petra Saskia Bayerl
Keyword(s):  
Pilot Study ◽  
Statistical Approach ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Discourse Markers ◽  
Naïve Bayes ◽  
Word Sense ◽  
Wide Range ◽  
Sense Disambiguation ◽  
Automatic Text

Discourse markers such as German aber, wohl or obwohl can be regarded as valuable information for a wide range of text-linguistic applications, since they provide important cues for the interpretation of texts or text segments. Unfortunately, many of them are highly ambiguous. Thus, for their use in applications like automatic text summarizations a reliable disambiguation of discourse markers is needed. This should be done automatically, since manual disambiguation is feasible only for small amounts of data. The aim of this pilot study, therefore, was to investigate methodological requirements of automatic disambiguation of German discourse markers. Two different methods known from word-sense disambiguation, Naive-Bayes and decisionlists, were used for the highly ambiguous marker wenn. A statistical approach was taken to compare the two approaches and different feature combinations.

scholarly journals Deep learning-based landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Azarafza ◽  
Mehdi Azarafza ◽  
Haluk Akgün ◽  
Peter M. Atkinson ◽  
Reza Derakhshani
Keyword(s):  
Landslide Susceptibility ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Landslide Risk ◽  
Isfahan Province ◽  
Wide Range

AbstractLandslides are considered as one of the most devastating natural hazards in Iran, causing extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas can be used to plan for and mitigate the consequences of catastrophic landsliding events. Here, we developed a deep convolutional neural network (CNN–DNN) for mapping landslide susceptibility, and evaluated it on the Isfahan province, Iran, which has not previously been assessed on such a scale. The proposed model was trained and validated using training (80%) and testing (20%) datasets, each containing relevant data on historical landslides, field records and remote sensing images, and a range of geomorphological, geological, environmental and human activity factors as covariates. The CNN–DNN model prediction accuracy was tested using a wide range of statistics from the confusion matrix and error indices from the receiver operating characteristic (ROC) curve. The CNN–DNN model was evaluated comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The CNN–DNN model for landslide susceptibility mapping was found to predict more accurately than the benchmark algorithms, with an AUC = 90.9%, IRs = 84.8%, MSE = 0.17, RMSE = 0.40, and MAPE = 0.42. The map provided by the CNN–DNN clearly revealed a high-susceptibility area in the west and southwest, related to the main Zagros trend in the province. These findings can be of great utility for landslide risk management and land use planning in the Isfahan province.

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