A novel logistic-NARX model as a classifier for dynamic binary classification

System identification and data-driven modeling techniques have seen ubiquitous applications in the past decades. In particular, parametric modeling methodologies such as linear and nonlinear autoregressive with exogenous input models (ARX and NARX) and other similar and related model types have been preferably applied to handle diverse data-driven modeling problems due to their easy-to-compute linear-in-the-parameter structure, which allows the resultant models to be easily interpreted. In recent years, several variations of the NARX methodology have been proposed that improve the performance of the original algorithm. Nevertheless, in most cases, NARX models are applied to regression problems where all output variables involve continuous or discrete-time sequences sampled from a continuous process, and little attention has been paid to classification problems where the output signal is a binary sequence. Therefore, we developed a novel classification algorithm that combines the NARX methodology with logistic regression and the proposed method is referred to as logistic-NARX model. Such a combination is advantageous since the NARX methodology helps to deal with the multicollinearity problem while the logistic regression produces a model that predicts categorical outcomes. Furthermore, the NARX approach allows for the inclusion of lagged terms and interactions between them in a straight forward manner resulting in interpretable models where users can identify which input variables play an important role individually and/or interactively in the classification process, something that is not achievable using other classification techniques like random forests, support vector machines, and k-nearest neighbors. The efficiency of the proposed method is tested with five case studies

A novel logistic-NARX model as a classifier for dynamic binary classification

System identification and data-driven modeling techniques have seen ubiquitous applications in the past decades. In particular, parametric modeling methodologies such as linear and nonlinear autoregressive with exogenous input models (ARX and NARX) and other similar and related model types have been preferably applied to handle diverse data-driven modeling problems due to their easy-to-compute linear-in-the-parameter structure, which allows the resultant models to be easily interpreted. In recent years, several variations of the NARX methodology have been proposed that improve the performance of the original algorithm. Nevertheless, in most cases, NARX models are applied to regression problems where all output variables involve continuous or discrete-time sequences sampled from a continuous process, and little attention has been paid to classification problems where the output signal is a binary sequence. Therefore, we developed a novel classification algorithm that combines the NARX methodology with logistic regression and the proposed method is referred to as logistic-NARX model. Such a combination is advantageous since the NARX methodology helps to deal with the multicollinearity problem while the logistic regression produces a model that predicts categorical outcomes. Furthermore, the NARX approach allows for the inclusion of lagged terms and interactions between them in a straight forward manner resulting in interpretable models where users can identify which input variables play an important role individually and/or interactively in the classification process, something that is not achievable using other classification techniques like random forests, support vector machines, and k-nearest neighbors. The efficiency of the proposed method is tested with five case studies

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)

Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced dimensionality, as well as predicts categorical outputs. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with classical classification algorithms.Comment: In English. SBAI 2021 - Brazilian Symposium on Intelligent Automation (SBAI - Simposio Brasileiro de Automacao Inteligente). 6 pages. 4 figure

Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced dimensionality, as well as predicts categorical outputs. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with traditional classification algorithms

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio