Data Mining and Machine Learning Methods for Microarray Analysis

2002 ◽  
pp. 5-22 ◽  
Author(s):  
Werner Dubitzky ◽  
Martin Granzow ◽  
Daniel Berrar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Microarray Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

2018 ◽  
Vol 117 (3) ◽  
pp. 387-423
Author(s):  
Marco Götz ◽  
Ferenc Leichsenring ◽  
Thomas Kropp ◽  
Peter Müller ◽  
Tobias Falk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

scholarly journals Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

10.2196/12001 ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. e12001 ◽  
Author(s):  
Quazi Abidur Rahman ◽  
Tahir Janmohamed ◽  
Meysam Pirbaglou ◽  
Hance Clarke ◽  
Paul Ritvo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

Data mining/machine learning methods in foodomics

2021 ◽  
Vol 37 ◽  
pp. 76-82
Author(s):  
Ana M Jimenez-Carvelo ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mining Machine ◽  
Learning Methods ◽  
Machine Learning Methods

Comparison of Prediction Models for Delays of Trains by using Data Mining and Machine Learning Methods

2018 ◽  
Author(s):  
Dennis Leser ◽  
Matthias Wastian ◽  
Matthias Rößler ◽  
Michael Landsied
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

scholarly journals Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods (Preprint)

2018 ◽  
Author(s):  
Quazi Abidur Rahman ◽  
Tahir Janmohamed ◽  
Meysam Pirbaglou ◽  
Hance Clarke ◽  
Paul Ritvo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Random Forests ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Class Imbalance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Volatility

BACKGROUND Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review

2019 ◽  
Vol 122 ◽  
pp. 25-39 ◽  
Author(s):  
Ana M. Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
M. Gracia Bagur-González ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Food Quality ◽  
Mining Machine ◽  
Analytical Evaluation ◽  
Learning Methods ◽  
Machine Learning Methods
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