关键词： Prediction of electrical power output   Combined cycle power plants   Machine learning methods  

摘要： Predicting full load electrical power output of a base load power plant is important in order to maximize the profit from the available megawatt hours. This paper examines and compares some machine learning regression methods to develop a predictive model, which can predict hourly full load electrical power output of a combined cycle power plant. The base load operation of a power plant is influenced by four main parameters, which are used as input variables in the dataset, such as ambient temperature, atmospheric pressure, relative humidity, and exhaust steam pressure. These parameters affect electrical power output, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected over a six-year period. First, based on these variables the best subset of the dataset is explored among all feature subsets in the experiments. Then, the most successful machine learning regression method is sought for predicting full load electrical power output. Thus, the best performance of the best subset, which contains A complete set of input variables, has been observed using the most successful method, which is Bagging algorithm with REPTree, with a mean absolute error of 2.818 and a Root Mean-Squared Error of 3.787. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： machine learning   support vector machines   artificial neural networks   CART classification trees   k-nearest neighbour   large-dimensional data   cross-validation  

摘要： Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

关键词： Visual Field   Glaucoma   Quality of Life  

摘要： Background/aims We investigated whether it was useful to use machine learning algorithms to predict patients' vision related quality of life (VRQoL) from visual field (VF) and visual acuity (VA). Methods VRQoL was surveyed in 164 glaucomatous patients using the Sumi questionnaire. Their VRQoL score was predicted using machine learning algorithms (Random Forest, gradient boosting, support vector machine) based on total deviation (TD) values from integrated VF (IVF), VA, age and gender. For comparison, VRQoL score was predicted using standard linear regression with mean of IVF, TD values, and VA, and also the stepwise model selection by Akaike Information Criterion. Prediction error was calculated as root mean of the squared prediction error (RMSE) associated with the leave one out cross validation. Results RMSEs associated with general VRQoL score were smaller for the machine learning algorithms (1.99 to 2.21) compared with the standard linear model and the stepwise model selection (2.35 to 3.20). A similar tendency was found in each individual VRQoL task score. Conclusions We found that it was advantageous to use machine learning methods to predict VRQoL accurately. These statistical methods could be used to help clinicians better understand patients' VRQoL without the need for extra tests other than standard VA and VF.

关键词： drug discovery   drug-target interaction prediction   machine learning   drug similarity   target similarity  

摘要： Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.

关键词： Neural networks  

摘要： Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naive Bayes classifiers. (C) 2014 The Authors. WIREs Computational Molecular Science published by JohnWiley & Sons, Ltd.

关键词： Software quality   Static code metrics   Logistic regression   Machine learning   Receiver Operating Characteristic (ROC) curve  

摘要： The demand for development of good quality software has seen rapid growth in the last few years. This is leading to increase in the use of the machine learning methods for analyzing and assessing public domain data sets. These methods can be used in developing models for estimating software quality attributes such as fault proneness, maintenance effort, testing effort. Software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. This paper analyses and compares the statistical and six machine learning methods for fault prediction. These methods (Decision Tree, Artificial Neural Network, Cascade Correlation Network, Support Vector Machine, Group Method of Data Handling Method, and Gene Expression Programming) are empirically validated to find the relationship between the static code metrics and the fault proneness of a module. In order to assess and compare the models predicted using the regression and the machine learning methods we used two publicly available data sets AR1 and AR6. We compared the predictive capability of the models using the Area Under the Curve (measured from the Receiver Operating Characteristic (ROC) analysis). The study confirms the predictive capability of the machine learning methods for software fault prediction. The results show that the Area Under the Curve of model predicted using the Decision Tree method is 0.8 and 0.9 (for AR1 and AR6 data sets, respectively) and is a better model than the model predicted using the logistic regression and other machine learning methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Animal breeding   Cross validation   Genome wide prediction   Machine learning   Nonparametric   Predictive accuracy  

摘要： Genome-wide prediction of complex traits has become increasingly important in animal and plant breeding, and is receiving increasing attention in human genetics. Most common approaches are whole-genome regression models where phenotypes are regressed on thousands of markers concurrently, applying different prior distributions to marker effects. While use of shrinkage or regularization in SNP regression models has delivered improvements in predictive ability in genome-based evaluations, serious over-fitting problems may be encountered as the ratio between markers and available phenotypes continues increasing. Machine learning is an alternative approach for prediction and classification, capable of dealing with the dimensionality problem in a computationally flexible manner. In this article we provide an overview of non-parametric and machine learning methods used in genome wide prediction, discuss their similarities as well as their relationship to some well-known parametric approaches. Although the most suitable method is usually case dependent, we suggest the use of support vector machines and random forests for classification problems, whereas Reproducing Kernel Hilbert Spaces regression and boosting may suit better regression problems, with the former having the more consistently higher predictive ability. Neural Networks may suffer from over-fitting and may be too computationally demanded when the number of neurons is large. We further discuss on the metrics used to evaluate predictive ability in model comparison under cross-validation from a genomic selection point of view. We suggest use of predictive mean squared error as a main but not only metric for model comparison. Visual tools may greatly assist on the choice of the most accurate model. (C) 2014 Elsevier B.V. All rights reserved.

关键词： cancer biology   machine learning   support vector machines   LASSO algorithm   elastic net algorithm   compressed sensing  

摘要： The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

关键词： image analysis   fruit detection   machine learning   young fruit   tomato  

摘要： Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.

关键词： Roughness   Road pavement   Support vector machine   Random forest   Neural network   Weighted longitudinal profile   Machine learning   In-vehicle sensors   Vehicle dynamics   Simulation  

摘要： The roughness of a road pavement affects safety, ride comfort, and road durability. A useful indicator for evaluating roughness is the weighted longitudinal profile (wLP). In this paper, three machine learning models are compared for estimating the wLP when vehicle response information, i.e., accelerometer and wheel speed data is collected from common in-vehicle sensors. A multilayer perceptron, support vector machine (SVM) and random forest were applied for testing their effectiveness in estimating the key indices of wLP, namely, range and standard deviation. These models were trained from a set of features extracted from vehicle response simulations on accurate replications of roads with various roughness problems. In contrast to other research, the authors validated models with measurements collected with a probe vehicle. The results show that roughness phenomena can be accurately detected. The SVM produced the best results, although the models achieved rather similar performance. However, differences were found regarding the model robustness when reducing the size of the training feature set. The proposed method enables road network monitoring to be achieved by conventional passenger cars, which can be seen as a practical supplement to the prevalent road measurements with cost-intensive mobile devices. (C) 2014 American Society of Civil Engineers.