Machine Learning for Clinical Data Processing

Support Vector ◽

Data Sets ◽

The Novel ◽

Real World Data ◽

Data Set ◽

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

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

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.1019 ◽

2021 ◽

Author(s):

Gediminas Adomavicius ◽

Yaqiong Wang

Keyword(s):

Machine Learning ◽

General Purpose ◽

Reliability Estimation ◽

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

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 ◽

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.

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

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

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 ◽

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.

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 ◽

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.

Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

Industrial Management & Data Systems ◽

10.1108/imds-02-2018-0072 ◽

2019 ◽

Vol 119 (3) ◽

pp. 676-696 ◽

Cited By ~ 5

Author(s):

Zhongyi Hu ◽

Raymond Chiong ◽

Ilung Pranata ◽

Yukun Bao ◽

Yuqing Lin

Keyword(s):

Machine Learning ◽

Class Imbalance ◽

Performance Data ◽

Data Sets ◽

Real World Data ◽

Content Type ◽

Domain Identification ◽

Learning Techniques ◽

And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Vehicle Price Prediction using SVM Techniques

10.35940/ijitee.g5915.069820 ◽

2020 ◽

Vol 9 (8) ◽

pp. 398-401

Keyword(s):

Machine Learning ◽

Research Area ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Network Support ◽

Java Application ◽

Learning Techniques ◽

The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Studies in Engineering and Technology ◽

10.11114/set.v8i1.5213 ◽

2021 ◽

Vol 8 (1) ◽

pp. 28

Author(s):

S. L. Ávila ◽

H. M. Schaberle ◽

S. Youssef ◽

F. S. Pacheco ◽

C. A. Penz

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Signature Analysis ◽

Data Set ◽

Learning Techniques ◽

Environmental Variations ◽

Current Signature

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients

Scientific Reports ◽

10.1038/s41598-021-92287-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hua Liu ◽

Hua Yuan ◽

Yongmei Wang ◽

Weiwei Huang ◽

Hui Xue ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Support Vector Machine Model ◽

Adverse Outcomes ◽

Support Vector ◽

Data Sets ◽

Middle Aged ◽

Machine Model ◽

AbstractAccumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806–0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.

Automated Amharic News Categorization Using Deep Learning Models

Computational Intelligence and Neuroscience ◽

10.1155/2021/3774607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Demeke Endalie ◽

Getamesay Haile

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Document Classification ◽

Machine Learning Algorithms ◽

Support Vector ◽

Language Resources ◽

Data Set ◽

Learning Techniques ◽

Proposed Model

For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.