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Linear Discriminant Analysis (LDA) yields the optimal Bayes classifier for binary classification for normally distributed classes with equal covariance. To improve the performance of LDA, heteroscedastic LDA (HLDA) that removes the equal covariance assumption has been developed. In this paper, we show using first and second-order optimality conditions that the existing approaches either have no principled computational procedure for optimal parameter selection, or underperform in terms of the accuracy of classification and the area under the receiver operating characteristics curve (AUC) under class imbalance. Using the same optimality conditions, we then derive a dynamic Bayes optimal linear classifier for heteroscedastic LDA that is optimised via an efficient iterative procedure, which is robust against class imbalance. Experimental work is conducted on two artificial and eight real-world datasets. Our results show that the proposed algorithm compares favourably with the existing heteroscedastic LDA procedures as well as the linear support vector machine (SVM) in terms of the error rate, but is superior to all the algorithms in terms of the AUC under class imbalance. The fast training time of the proposed algorithm also encourages its use for large-data applications that show high incidence of class imbalance, such as in human activity recognition.

BACKGROUND: For passengers in private or public transportation systems, comfort is a major interest. Available comfort models are already used to correlate thermal comfort to influencing factors. However, the available models do not other sensory comfort aspect and specific influences as fragrances and ambient light. OBJECTIVE: This publication investigates the impact of fragrances with “warm” and “cold” associated meanings on thermal and overall comfort perception. METHODS: Human subject trials (n = 47) were performed in different temperature-controlled environments following a 3×3 within-subject design considering ambient fragrance (“neutral scent”, “peppermint”, “orange & cinnamon”) and ambient light as variables. RESULTS: Olfactory comfort is shown to have the larger effect on overall comfort perception, comparable in weight to the one of thermal comfort. The impact observed on thermal sensation was in line with the meanings associated the fragrances, whereas it was positive on thermal comfort appreciation regardless of the type of fragrance diffused. CONCLUSIONS: These initial results suggest that olfactory stimulations have the potential to positively impact thermal and overall comfort. The appreciation of the fragrance appears to have a major impact on these interactions and should be deeply considered in future research and features development.

We propose a supervised linear dimensionality reduction algorithm.The algorithm reduces the dimensionality of data to K1 for the K-class problem.The linearly reduced data is well-suited for Bayesian classification.Experiments on UCI datasets are performed for the proposed and existing algorithms.The applicability of the algorithm to flow meter diagnostics is also demonstrated. Supervised linear dimensionality reduction (LDR) performed prior to classification often improves the accuracy of classification by reducing overfitting and removing multicollinearity. If a Bayes classifier is to be used, then reduction to a dimensionality of K1 is necessary and sufficient to preserve the classification information in the original feature space for the K-class problem. However, most of the existing algorithms provide no optimal dimensionality to which to reduce the data, thus classification information can be lost in the reduced space if K1 dimensions are used. In this paper, we present a novel LDR technique to reduce the dimensionality of the original data to K1, such that it is well-primed for Bayesian classification. This is done by sequentially constructing linear classifiers that minimise the Bayes error via a gradient descent procedure, under an assumption of normality. We experimentally validate the proposed algorithm on 10 UCI datasets. Our algorithm is shown to be superior in terms of the classification accuracy when compared to existing algorithms including LDR based on Fishers criterion and the Chernoff criterion. The applicability of our algorithm is then demonstrated by employing it in diagnosing the health states of 2 ultrasonic flow meters. As with the UCI datasets, the proposed algorithm is found to have superior performance to the existing algorithms, achieving classification accuracies of 99.4% and 97.5% on the two flow meters. Such high classification accuracies on the flow meters promise significant cost benefits in oil and gas operations.

We derive a linear classifier for heteroscedastic linear discriminant analysis.The proposed scheme efficiently minimises the Bayes error for binary classification.A local neighbourhood search is also proposed for non-normal distributions.The proposed schemes are experimentally validated on twelve datasets. Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.

Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This paper presents findings from surveys that were carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and more generally discuss the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that the fuel choice determinants are the age of household, the number of people at meals each day, the number of meals daily, the community, education of the household head, and the income level of the household. Moreover, given the Brazilian policies related to energy and sustainability, this region is not likely to reach the Sustainable Development Goals proposed by United Nations for 2030.

An initial solution based on lattice reduction (LR) and a novel stopping criterion for the tabu search (TS) detection algorithm are proposed for use in multiple input multiple output (MIMO) antenna systems. LR aided-detection is performed and its result is utilised as an initial solution to the TS algorithm, while a measure of the condition of the lattice-reduced channel is used as a criterion for early termination of the algorithm. On the basis of the simulation results, this approach outperforms other initial solutions by about 3.5 dB at a BER of 10−3 for the 16-QAM 16 × 16 MIMO with only a slight increment in computational cost. The proposed stopping criterion reduces the complexity further with virtually no loss in BER performance.