Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.info:eu-repo/semantics/publishedVersio

Feature selection for bankruptcy prediction: a multi-objective optimization approach

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – was fully analyzed using two classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously, the parameters required by both classifiers were also optimized. The validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The method proposed can provide useful information for the decision maker in characterizing the financial health of a company

Financial distress model prediction using SVM +

Financial distress prediction is of great importance to all stakeholders in order to enable better decision-making in evaluating firms. In recent years, the rate of bankruptcy has risen and it is becoming harder to estimate as companies become more complex and the asymmetric information between banks and firms increases. Although a great variety of techniques have been applied along the years, no comprehensive method incorporating an holistic perspective had hitherto been considered. Recently, SVM+ a technique proposed by Vapnik [17] provides a formal way to incorporate privileged information onto the learning models improving generalization. By exploiting additional information to improve traditional inductive learning we propose a prediction model where data is naturally separated into several groups according to the size of the firm. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed model showed superior performance in terms of prediction accuracy in bankruptcy prediction and misclassification cost.This work was partially supported by Fundacao da Ciencia e Tecnologia' under grant no.PTDC/GES/70168/2006

Stock Market Random Forest-Text Mining (SMRF-TM) Approach to Analyse Critical Indicators of Stock Market Movements

The Stock Market is a significant sector of a country’s economy and has a crucial role in the growth of commerce and industry. Hence, discovering efficient ways to analyse and visualise stock market data is considered a significant issue in modern finance. The use of data mining techniques to predict stock market movements has been extensively studied using historical market prices but such approaches are constrained to make assessments within the scope of existing information, and thus they are not able to model any random behaviour of the stock market or identify the causes behind events. One area of limited success in stock market prediction comes from textual data, which is a rich source of information. Analysing textual data related to the Stock Market may provide better understanding of random behaviours of the market. Text Mining combined with the Random Forest algorithm offers a novel approach to the study of critical indicators, which contribute to the prediction of stock market abnormal movements. In this thesis, a Stock Market Random Forest-Text Mining system (SMRF-TM) is developed and is used to mine the critical indicators related to the 2009 Dubai stock market debt standstill. Random forest and expectation maximisation are applied to classify the extracted features into a set of meaningful and semantic classes, thus extending current approaches from three to eight classes: critical down, down, neutral, up, critical up, economic, social and political. The study demonstrates that Random Forest has outperformed other classifiers and has achieved the best accuracy in classifying the bigram features extracted from the corpus

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

Data-Efficient Machine Learning with Focus on Transfer Learning

Machine learning (ML) has attracted a significant amount of attention from the artifi- cial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain dis- crepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) has been considered as an effective solution to address the three shortcomings of conventional ML. Furthermore, TL is one of the most active areas in the DEML. Over the past ten years, significant progress has been made in TL. In this dissertation, I propose to address the three problems by developing a software- oriented framework and TL algorithms. Firstly, I introduce a DEML framework and a evaluation system. Moreover, I present two novel TL algorithms and applications on real-world problems. Furthermore, I will first present the first well-defined DEML framework and introduce how it can address the challenges in ML. After that, I will give an updated overview of the state-of-the-art and open challenges in the TL. I will then introduce two novel algorithms for two of the most challenging TL topics: distant domain TL and cross-modality TL (image-text). A detailed algorithm introduction and preliminary results on real-world applications (Covid-19 diagnosis and image clas- sification) will be presented. Then, I will discuss the current trends in TL algorithms and real-world applications. Lastly, I will present the conclusion and future research directions

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier