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Analysis of NIR spectroscopic data using decision trees and their ensembles
摘要： Decision trees and their ensembles became quite popular for data analysis during the past decade. One of the main reasons for that is current boom in big data, where traditional statistical methods (such as, e.g., multiple linear regression) are not very efficient. However, in chemometrics these methods are still not very widespread, first of all because of several limitations related to the ratio between number of variables and observations. This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification. We will try to consider all important aspects including optimization and validation of models, evaluation of results, treating missing data and selection of most important variables. The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.
关键词： Decision trees,Classification and regression trees,Random forests,NIR spectroscopy
更新于2025-09-23 15:23:52
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[IEEE 2019 29th Australasian Universities Power Engineering Conference (AUPEC) - Nadi, Fiji (2019.11.26-2019.11.29)] 2019 29th Australasian Universities Power Engineering Conference (AUPEC) - Adaptive Boosting and Bootstrapped Aggregation based Ensemble Machine Learning Methods for Photovoltaic Systems Output Current Prediction
摘要： Photovoltaics output current prediction received great deal of attention in recent years, due to the high penetration level of PV utilization. The intermittent nature of PV systems, in addition to the fast-varying irradiance levels, provoked the need for fast, accurate and reliable forecasting techniques. Machine Learning (ML) methods have been proven to effectively solve regression-based prediction problems. ML methods that utilize multiple models to construct decision trees are called Ensemble Machine Learning (EML) algorithms. This paper presents a comparison study of two EML methods namely; AdaBoost and Random Forest for photovoltaics application. A dataset of fast varying environmental conditions has been employed and the terminal current of the experimental setup has been augmented based on the mathematical model and the use of an evolutionary algorithm. The mathematical model has been examined for several irradiance and temperature levels and adjusted based on the manufacturer datasheet. Random Forest overall absolute error distribution had the lowest mean and standard deviation. Results shows the superior performance of Random Forest over AdaBoost in terms of absolute error, on the contrary, AdaBoost absolute error distribution is scattered with larger quartiles limits. Random Forest overall absolute error distribution had the lowest mean of 0.27% with a standard deviation of 0.91%, however, AdaBoost absolute error mean was as high as 34.5% with a standard deviation of 15.8% relative to the mathematical model. Accurate predictions can be integrated in an EML based maximum power point tracking (MPPT) scheme.
关键词： ensemble machine learning,adaptive boosting,photovoltaics,regression decision trees,single diode model
更新于2025-09-23 15:21:01
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[IEEE 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - Novosibirsk, Russia (2019.10.21-2019.10.27)] 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - The Use of Spread Spectrum Signals to Increase the Noise Immunity of Optical Communication Systems Based on the Effect of LED Reversibility
摘要： Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this paper, four different classi?cation techniques—decision tree algorithms, support vector machines, arti?cial neural networks, and discriminant analysis—are used to build classi?er models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and speci?city performance metrics. Although all the classi?er models show comparably high classi?cation performances, C5.0 classi?er is the best with respect to accuracy, precision, and speci?city. In addition, an analysis of the variable importance for each classi?er model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The ?ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ?ndings may be used to improve the measurement instruments.
关键词： linear discriminant analysis,Arti?cial neural networks,support vector machines,decision trees,classi?cation algorithms,performance evaluation
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis
摘要： Many current studies conclude that facial attractiveness perception is data-based and irrespective of the perceiver. However, analyses of facial geometric image data and its visual impact always exceeded power of classical statistical methods. In this study, we have applied machine-learning methods to identify geometric features of a face associated with an increase of facial attractiveness after undergoing rhinoplasty. Furthermore, we explored how accurate classification of faces into sets of facial emotions and their facial manifestations is, since categorization of human faces into emotions manifestation should take into consideration the fact that total face impression is also dependent on expressed facial emotion. Both profile and portrait facial image data were collected for each patient (n = 42), processed, landmarked and analysed using R language. Multivariate linear regression was performed to select predictors increasing facial attractiveness after undergoing rhinoplasty. The sets of used facial emotions originate from Ekman-Friesen FACS scale, but was improved substantially. Bayesian naive classifiers, decision trees (CART) and neural networks were learned to allow assigning a new face image data into one of facial emotions. Enlargements of both a nasolabial and nasofrontal angle within rhinoplasty were determined as significant predictors increasing facial attractiveness (p < 0.05). Neural networks manifested the highest predictive accuracy of a new face classification into facial emotions. Geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order final classified emotion, as was identified using decision trees. We performed machine-learning analyses to point out which facial geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be treated within plastic surgeries.
关键词： Bayes naive classifier,facial emotions,decision trees,rhinoplasty,facial attractiveness,neural networks,machine learning
更新于2025-09-10 09:29:36