Improving binary classification using filtering based on k-NN proximity graphs

© 2020, The Author(s). One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters selection was adopted as the solution of outlier minimization problem: k-NN proximity graph, power of distance and threshold parameters are selected in order to minimize outlier percentage in training data. Then performance of six commonly used classifiers (Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine and Decision Tree) and one heterogeneous classifiers combiner (DES-LA) are compared with and without filtering. Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of base classifiers, such as, its accuracy in local regions of the feature space around the query instance. In our case the combiner is based on the local accuracy of single classifiers and its output is a linear combination of single classifiers ranking. As results of filtering, accuracy of DES-LA combiner shows big increase for low-accuracy datasets. But filtering doesn’t have sufficient impact on DES-LA performance while working with high-accuracy datasets. The results are discussed, and classifiers, which performance was highly affected by pre-processing filtering step, are defined. The main contribution of the paper is introducing modifications to the DES-LA combiner, as well as comparative analysis of filtering impact on the classifiers of various type. Testing the filtering algorithm on real case dataset (Taiwan default credit card dataset) confirmed the efficiency of automatic filtering approach

Modelling customers credit card behaviour using bidirectional LSTM neural networks

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring

A deep learning model for behavioural credit scoring in banks

The main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank’s decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring

Lean-green manufacturing practices and their link with sustainability: a critical review

The current rapidly changing and highly competitive market has put companies under a great pressure towards adopting sustainable practices, in terms of keeping a healthy balance among economic, environmental and social performances. In this context, the lean-green manufacturing approach, which combines lean practices focused on customers’ demand, and green practices focused on reducing the business’ environmental impact, has gained popularity. Nevertheless, the lean-green manufacturing is still a relatively new practice, lacking a clear and structured research definition, and of significant evidence of successful cases in the practice. In this paper, a literature review is conducted to identify the actual possibility of combining lean and green practices, the current trends for implementing such combination and the potential sustainability improvements such implementation can lead. It is the authors’ intention that the findings analysed in this paper can contribute to the state-of-the-art of lean-green manufacturing and provide practitioners with a useful tool towards developing effective strategies for its deployment

Data-driven based HVAC optimisation approaches: A Systematic Literature Review

Improving the energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial to reduce buildings’ energy costs and their carbon footprint. HVAC systems are complex, large-scale structures with pure lag time and high thermal inertia. Although traditionally, physical-based methods have been used to model, control and optimise them, data-driven approaches have demonstrated to be more application relevant, easier to compute and better suited to handle nonlinearities. Based only on measured or estimated data, data-driven approaches are highly dependent on the quality of the used data. In recent years, the advances in Information and Communication Technology (ICT), decreasing hardware cost, and improving data accessibility, have allowed the collection and storage of a large amount of high-quality building-related data, allowing the development of more accurate and robust data-driven approaches, making them gain great popularity in HVAC applications. In this paper, a Systematic Literature Review (SLR) based on a database search is conducted to give an in-depth insight into the major challenges regarding modelling, controlling and optimising HVAC systems, making the especial focus on the capability of data-driven models to improve their energy performance while keeping the users’ comfort. The main results of the SLR highlight the importance of taking users’ needs into account when modelling, controlling and optimising HVAC systems to avoid their underutilisation. In particular, the increasing tendency to include users’ feedback into Model Predictive Control (MPC) loops and use easy-to-access technologies, such as WiFi and Smartphone Applications (Apps), to acquire users’ information suggests promising future research horizons