A multilayer-perceptron based method for variable selection in soft sensor design

Souza, Francisco; Araújo, Rui; Matias, Tiago; Mendes, Jérôme

doi:10.1016/j.jprocont.2013.09.014

Cited by 37 publications

(15 citation statements)

References 21 publications

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“…MLP has several parameters or features that could be updated according to the nature of the data [34]. MLP also has good performance in pattern recognition in deep and complex studies.…”

Section: Resultsmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

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“…MLP has several parameters or features that could be updated according to the nature of the data [34]. MLP also has good performance in pattern recognition in deep and complex studies.…”

Section: Resultsmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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“…Hence, the features that are selected and useful in a ML algorithm can turn out to be less useful and important in a regression-based model. We applied six methods to extract the most common and important features, including logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO), 25 logistic regression with Elastic Net regularization, 26 Gradient Boosting machine (GBM), 27 Random Forest, Information Gain (IG) 28 and Multi-Layer Perceptron (MLP) 29 to find the most important features as predictors for designing a more accurate prediction model. For the LASSO, glmnet 30 package was used in R. In addition, for the IG method we applied WEKA data mining software ( https://www.cs.waikato.ac.nz/ml/weka/ ).…”

Section: Methodsmentioning

confidence: 99%

Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods

Jamshidi

Leclercq

Labbe

et al. 2020

Therapeutic Advances in Musculoskeletal

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Objectives: The aim was to identify the most important features of structural knee osteoarthritis (OA) progressors and classification using machine learning methods. Methods: Participants, features and outcomes were from the Osteoarthritis Initiative. Features were from baseline (1107), including articular knee tissues (135) assessed by quantitative magnetic resonance imaging (MRI). OA progressors were ascertained by four outcomes: cartilage volume loss in medial plateau at 48 and 96 months (Prop_CV_48M, 96M), Kellgren–Lawrence (KL) grade ⩾ 2 and medial joint space narrowing (JSN) ⩾ 1 at 48 months. Six feature selection models were used to identify the common features in each outcome. Six classification methods were applied to measure the accuracy of the selected features in classifying the subjects into progressors and non-progressors. Classification of the best features was done using an automatic machine learning interface and the area under the curve (AUC). To prioritize the top five features, sparse partial least square (sPLS) method was used. Results: For the classification of the best common features in each outcome, Multi-Layer Perceptron (MLP) achieved the highest AUC in Prop_CV_96M, KL and JSN (0.80, 0.88, 0.95), and Gradient Boosting Machine for Prop_CV_48M (0.70). sPLS showed the baseline top five features to predict knee OA progressors are the joint space width, mean cartilage thickness of the medial tibial plateau and sub-regions and JSN. Conclusion: In this comprehensive study using a large number of features ( n = 1107) and MRI outcomes in addition to radiological outcomes, we identified the best features and classification methods for knee OA structural progressors. Data revealed baseline X-ray and MRI-based features could predict early OA knee progressors and that MLP is the best classification method.

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“…Their approach was experimentally evaluated in four data-sets in a real-world meteorological data-set, which led to interesting results. Souza et al (2013) propose a new method for variable selection for prediction settings and soft sensor applications that are based on the MLP NN model for the variable selection method. The results indicate that in the soft sensor applications with a lower number of variables, there was a positive factor for decreasing implementation costs and even for making the soft sensor feasible at all.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Cheng

Wang

2015

Quantitative Finance

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Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts' opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data:(1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.

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A multilayer-perceptron based method for variable selection in soft sensor design

Cited by 37 publications

References 21 publications

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

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