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

Support Vector ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

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.

Do Data Mining Methods Matter? : A Wolfcamp “Shale” Case Study

10.2118/spe-173334-ms ◽

2015 ◽

Author(s):

Ming Zhong ◽

Jared Schuetter ◽

Srikanta Mishra ◽

Randy F. LaFollette

Keyword(s):

Machine Learning ◽

Data Mining ◽

Production Optimization ◽

Support Vector ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Study Results

Abstract Data mining for production optimization in unconventional reservoirs brings together data from multiple sources with varying levels of aggregation, detail, and quality. Tens of variables are typically included in data sets to be analyzed. There are many statistical and machine learning techniques that can be used to analyze data and summarize the results. These methods were developed to work extremely well in certain scenarios but can be terrible choices in others. The analyst may or may not be trained and experienced in using those methods. The question for both the analyst and the consumer of data mining analyses is, “What difference does the method make in the final interpreted result of an analysis?” The objective of this study was to compare and review the relative utility of several univariate and multivariate statistical and machine learning methods in predicting the production quality of Permian Basin Wolfcamp Shale wells. The data set for the study was restricted to wells completed in and producing from the Wolfcamp. Data categories used in the study included the well location and assorted metrics capturing various aspects of the well architecture, well completion, stimulation, and production. All of this information was publicly available. Data variables were scrutinized and corrected for inconsistent units and were sanity checked for out-of-bounds and other “bad data” problems. After the quality control effort was completed, the test data set was distributed among the statistical team for application of an agreed upon set of statistical and machine learning methods. Methods included standard univariate and multivariate linear regression as well as advanced machine learning techniques such as Support Vector Machine, Random Forests, and Boosted Regression Trees. The strengths, limitations, implementation, and study results of each of the methods tested are discussed and compared to those of the other methods. Consistent with other data mining studies, univariate linear methods are shown to be much less robust than multivariate non-linear methods, which tend to produce more reliable results. The practical importance is that when tens to hundreds of millions of dollars are at stake in the development of shale reservoirs, operators should have the confidence that their decisions are statistically sound. The work presented here shows that methods do matter, and useful insights can be derived regarding complex geosystem behavior by geoscientists, engineers, and statisticians working together.

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 ◽

Support Vector ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

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.

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 ◽

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.

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

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Training Data ◽

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.

Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

10.1101/2021.06.10.447991 ◽

2021 ◽

Author(s):

Rogini Runghen ◽

Daniel B Stouffer ◽

Giulio Valentino Dalla Riva

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Link Prediction ◽

Graph Embedding ◽

Feature Space ◽

Large Networks ◽

Data Set ◽

Learning Techniques ◽

Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽

10.3390/electronics10222857 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2857

Author(s):

Laura Vigoya ◽

Diego Fernandez ◽

Victor Carneiro ◽

Francisco Nóvoa

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

False Positive Rate ◽

Support Vector ◽

High Detection Rate ◽

Security Vulnerabilities ◽

Smart Systems ◽

Learning Techniques ◽

Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines

Data Science and Predictive Analytics ◽

10.1007/978-3-319-72347-1_11 ◽

2018 ◽

pp. 383-422 ◽

Cited By ~ 1

Author(s):

Ivo D. Dinov

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machines ◽

Black Box ◽

Support Vector ◽

Learning Methods ◽

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

Remote Sensing ◽

10.3390/rs11161943 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1943 ◽

Cited By ~ 15

Author(s):

Omid Rahmati ◽

Saleh Yousefi ◽

Zahra Kalantari ◽

Evelyn Uuemaa ◽

Teimur Teimurian ◽

...

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Characteristic Curve ◽

Support Vector ◽

Mountainous Area ◽

Data Set ◽

Boosted Regression Tree ◽

Hazard Exposure ◽

Learning Techniques

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

Combining Correlation-Based Feature and Machine Learning for Sensory Evaluation of Saigon Beer

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 71-85

Author(s):

Nhat-Vinh Lu ◽

Trong-Nhan Vuong ◽

Duy-Tai Dinh

Keyword(s):

Machine Learning ◽

Sensory Evaluation ◽

Support Vector ◽

Learning Methods ◽

Feature Selection Technique ◽

Learning Techniques ◽

Correlation Based Feature Selection ◽

Positive Results

Sensory evaluation plays an important role in the food and consumer goods industry. In recent years, the application of machine learning techniques to support food sensory evaluation has become popular. Many different machine learning methods have been applied and produced positive results in this field. In this article, the authors propose a new method to support sensory evaluation on multiple criteria based on the use of a correlation-based feature selection technique, combined with machine learning methods such as linear regression, multilayer perceptron, support vector machine, and random forest. Experimental results are based on considering the correlation between physicochemical components and sensory factors on the Saigon beer dataset.

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 ◽

Support Vector ◽

Traditional Methods ◽

Actual Result ◽

Learning Techniques ◽

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.