Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

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.

Download Full-text

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Scientific Reports ◽

10.1038/s41598-021-87834-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tom Elliot ◽

Robert Morse ◽

Duane Smythe ◽

Ashley Norris

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Random Forest ◽

Objective Evaluation ◽

Classification Performance ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Vector Machines

AbstractIt is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

Download Full-text

Building Bayesian network classifiers through a Bayesian complexity monitoring system

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes1243 ◽

2008 ◽

Vol 223 (3) ◽

pp. 743-755 ◽

Cited By ~ 10

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.

Download Full-text

Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Methods ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

Download Full-text

Big Data: New Tricks for Econometrics

The Journal of Economic Perspectives ◽

10.1257/jep.28.2.3 ◽

2014 ◽

Vol 28 (2) ◽

pp. 3-28 ◽

Cited By ~ 443

Author(s):

Hal R. Varian

Keyword(s):

Big Data ◽

Linear Models ◽

Neural Nets ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Model Complex ◽

Vector Machines ◽

Sheer Size ◽

Complex Relationships

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

Download Full-text

Heart disease prediction using machine learning techniques : a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10557 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 684 ◽

Cited By ~ 12

Author(s):

V V. Ramalingam ◽

Ayantan Dandapath ◽

M Karthik Raja

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Complex Data ◽

Learning Techniques ◽

Vector Machines ◽

Supervised Learning Algorithms ◽

Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Download Full-text

A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

Implementing Computational Intelligence Techniques for Security Systems Design - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2418-3.ch007 ◽

2020 ◽

pp. 146-162

Author(s):

Hesham M. Al-Ammal

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Anomaly Detection ◽

Real Life ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Vector Machines

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

Download Full-text

Twitter sentiment analysis for the estimation of voting intention in the 2017 Chilean elections

Intelligent Data Analysis ◽

10.3233/ida-194768 ◽

2020 ◽

Vol 24 (5) ◽

pp. 1141-1160

Author(s):

Tomás Alegre Sepúlveda ◽

Brian Keith Norambuena

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Sentiment Analysis ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Traditional Methods ◽

Actual Result ◽

Learning Techniques ◽

Vector Machines

In this paper, we apply sentiment analysis methods in the context of the first round of the 2017 Chilean elections. The purpose of this work is to estimate the voting intention associated with each candidate in order to contrast this with the results from classical methods (e.g., polls and surveys). The data are collected from Twitter, because of its high usage in Chile and in the sentiment analysis literature. We obtained tweets associated with the three main candidates: Sebastián Piñera (SP), Alejandro Guillier (AG) and Beatriz Sánchez (BS). For each candidate, we estimated the voting intention and compared it to the traditional methods. To do this, we first acquired the data and labeled the tweets as positive or negative. Afterward, we built a model using machine learning techniques. The classification model had an accuracy of 76.45% using support vector machines, which yielded the best model for our case. Finally, we use a formula to estimate the voting intention from the number of positive and negative tweets for each candidate. For the last period, we obtained a voting intention of 35.84% for SP, compared to a range of 34–44% according to traditional polls and 36% in the actual elections. For AG we obtained an estimate of 37%, compared with a range of 15.40% to 30.00% for traditional polls and 20.27% in the elections. For BS we obtained an estimate of 27.77%, compared with the range of 8.50% to 11.00% given by traditional polls and an actual result of 22.70% in the elections. These results are promising, in some cases providing an estimate closer to reality than traditional polls. Some differences can be explained due to the fact that some candidates have been omitted, even though they held a significant number of votes.

Download Full-text

A Probe into the Parallel Elements of Diabetes Mellitus and Alzheimer’s Disease

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9238 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3598-3604

Author(s):

M. S. Roobini ◽

M. Lakshmi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Morphological Changes ◽

Memory Loss ◽

Machine Learning Techniques ◽

Support Vector ◽

Huge Number ◽

Learning Techniques ◽

Vector Machines ◽

The Mind

Alzheimer’s Disease (AD) is a standout amongst the most familiar types of memory loss influencing a huge number of senior individuals around the world which is the main source of dementia and memory misfortune. AD causes shrinkage in hippocampus and cerebral cortex and it grows the ventricles in the mind Enhancing home and network based composed consideration is basic to alleviating Alzheimer’s impacts on people and families and to decreasing mounting medicinal services costs. Distinguishing early morphological changes in the mind and making early determination are vital for Alzheimer’s ailment (AD). A few machine learning techniques, for example, Support vector machines have been utilized and a portion of these strategies have been appeared to be extremely compelling in diagnosing AD from neuroimages, some of the time significantly more viable than human radiologists. MRI uncover the data of AD however decay districts are diverse for various individuals which makes the finding somewhat trickier. By utilizing Convolutional Neural Networks, the issue can be settled with insignificant mistake rate. This paper proposes a profound Convolutional Neural Network (CNN) for Alzheimer’s Disease finding utilizing mind MRI information examination. The calculation was prepared and tried utilizing the MRI information from Alzheimer’s Disease Neuroimaging Initiative.

Download Full-text

Predicting Takeover Success Using Machine Learning Techniques

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v10i10.7264 ◽

2012 ◽

Vol 10 (10) ◽

pp. 547

Author(s):

Mei Zhang ◽

Gregory Johnson ◽

Jia Wang

Keyword(s):

Machine Learning ◽

Learning Community ◽

Binary Classification ◽

Classification Problem ◽

Machine Learning Techniques ◽

Success Prediction ◽

Support Vector ◽

Font Size ◽

Network Support ◽

Learning Techniques

A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement. We perform a thorough study using machine learning techniques to predict takeover success. Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community. Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost. The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task. The result is consistent with the general observations of these two approaches.

Download Full-text

Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/7307803 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Jorge D. Mello-Román ◽

Julio C. Mello-Román ◽

Santiago Gómez-Guerrero ◽

Miguel García-Torres

Keyword(s):

Public Health ◽

Medical Diagnosis ◽

Machine Learning Techniques ◽

Support Vector ◽

The Public ◽

Learning Techniques ◽

Previous Diagnosis ◽

Vector Machines ◽

High Incidence

Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012–2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.

Download Full-text