Building Bayesian network classifiers through a Bayesian complexity monitoring system

2008 ◽  
Vol 223 (3) ◽  
pp. 743-755 ◽  
Author(s):  
G A Ruz ◽  
D T Pham
Keyword(s):  
Bayesian Network ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Learning Method ◽  
Benchmark Data ◽  
Bayesian Network Classifiers ◽  
Tree Construction ◽  
Learning Techniques ◽  
Bayesian Network Classifier

Nowadays, the need for practical yet efficient machine learning techniques for engineering applications are highly in demand. A new learning method for building Bayesian network classifiers is presented in this article. The proposed method augments the naive Bayesian (NB) classifier by using the Chow and Liu tree construction method, but introducing a Bayesian approach to control the accuracy and complexity of the resulting network, which yields simple structures that are not necessarily a spanning tree. Experiments by using benchmark data sets show that the number of augmenting edges by using the proposed learning method depends on the number of training data used. The classification accuracy was better, or at least equal, to the NB and the tree augmented NB models when tested on 10 benchmark data sets. The evaluation on a real industrial application showed that the simple Bayesian network classifier outperformed the C4.5 and the random forest algorithms and achieved competitive results against C5.0 and a neural network.

scholarly journals Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gonzalo A. Ruz ◽  
Pamela Araya-Díaz
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Classification Problem ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Bayesian Network Classifiers ◽  
Tree Construction ◽  
Learning Techniques ◽  
Vector Machines

Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.

Application of multi-sensor unmanned aerial system for identification of hydrothermal alteration zones

2020 ◽  
Author(s):  
Yosoon Choi ◽  
Jieun Baek ◽  
Jangwon Suh ◽  
Sung-Min Kim
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Training Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Integrated Analysis ◽  
Unmanned Aerial System ◽  
Data Sets ◽  
Learning Techniques ◽  
Hydrothermal Alteration Zones

<p>In this study, we proposed a method to utilize a multi-sensor Unmanned Aerial System (UAS) for exploration of hydrothermal alteration zones. This study selected an area (10m × 20m) composed mainly of the andesite and located on the coast, with wide outcrops and well-developed structural and mineralization elements. Multi-sensor (visible, multispectral, thermal, magnetic) data were acquired in the study area using UAS, and were studied using machine learning techniques. For utilizing the machine learning techniques, we applied the stratified random method to sample 1000 training data in the hydrothermal zone and 1000 training data in the non-hydrothermal zone identified through the field survey. The 2000 training data sets created for supervised learning were first classified into 1500 for training and 500 for testing. Then, 1500 for training were classified into 1200 for training and 300 for validation. The training and validation data for machine learning were generated in five sets to enable cross-validation. Five types of machine learning techniques were applied to the training data sets: k-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). As a result of integrated analysis of multi-sensor data using five types of machine learning techniques, RF and SVM techniques showed high classification accuracy of about 90%. Moreover, performing integrated analysis using multi-sensor data showed relatively higher classification accuracy in all five machine learning techniques than analyzing magnetic sensing data or single optical sensing data only.</p>

scholarly journals Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Dieter Devlaminck ◽  
Bart Wyns ◽  
Moritz Grosse-Wentrup ◽  
Georges Otte ◽  
Patrick Santens
Keyword(s):  
Motor Imagery ◽  
Spatial Filter ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Calibration Data ◽  
Common Spatial Pattern ◽  
Computer Interfaces ◽  
Learning Techniques ◽  
The Common

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

scholarly journals Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 537
Author(s):  
Zhi-Yi Duan ◽  
Li-Min Wang ◽  
Musa Mammadov ◽  
Hua Lou ◽  
Ming-Hui Sun
Keyword(s):  
Classification Performance ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Experimental Comparison ◽  
Final Model ◽  
Conditional Dependence ◽  
Bayesian Network Classifiers ◽  
Learning Techniques ◽  
Class Labels ◽  
Unlabeled Instance

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias. To address this issue, we propose a novel framework, called discriminatory target learning, which can be regarded as a tradeoff between probabilistic model learned from unlabeled instance at the uncertain end and that learned from labeled training data at the certain end. The final model can discriminately represent the dependence relationships hidden in unlabeled instance with respect to different possible class labels. Taking k-dependence Bayesian classifier as an example, experimental comparison on 42 publicly available datasets indicated that the final model achieved competitive classification performance compared to state-of-the-art learners such as Random forest and averaged one-dependence estimators.

scholarly journals Aircraft Aviation System Environment Impact Factors Prediction using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 2526-2530
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Impact Factors ◽  
Data Sets ◽  
Paper Machine ◽  
Probabilistic Prediction ◽  
Environment Impact ◽  
Decision Systems ◽  
Learning Techniques

Aircraft aviation system modules are considered for eco friendly oriented service estimation by global organizations. The emissions and aerodrome infrastructures effects the environment and citizen areas surrounding to aerodromes. An interest in researching to identify substantial environmental impact factors by authorities to support Eco-systems increased. In this paper Machine-Learning techniques applied over various training data sets related to aircraft aviation systems to generate interesting patterns related to environmental effects by aircrafts. Probabilistic prediction algorithms applied to support decision systems in generating guidelines to enhance the Eco-friendly architectures of aerodromes as well as aircrafts. The factors identification and territorial based environment precautions deviation observed for locating Eco-system regulation needed zones. The classifications performed in this paper over aircraft systems generate interesting measures to classify environmental scalable aircrafts in future with better eco-friendly technology. Rule miners identify the zones attributes associations among various countries. The work projected in this paper supports aircraft organizations to accurately estimate the environmental effect scores for aviation systems

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

2021 ◽  
Author(s):  
Gediminas Adomavicius ◽  
Yaqiong Wang
Keyword(s):  
Machine Learning ◽  
General Purpose ◽  
Reliability Estimation ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Learning Techniques ◽  
Reliability Indicator ◽  
Machine Learning Approach ◽  
Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

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