A hybrid model combining neural networks and decision tree for comprehension detection

OrShea, James; Crockett, Keeley; Khan, Wasiq; Bandar, Zuhair

doi:10.1109/ijcnn.2018.8489621

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…They could not for example be replicated by a human. As the problem is complex, the tree is large -previous work [39] suggests pruning may lead up to a 25% reduction in rules. A sacrifice in classification accuracy occurs but still the quantity of rules is large and difficult to comprehend.…”

Section: Results and Analysismentioning

confidence: 99%

Reconciling Adapted Psychological Profiling with the New European Data Protection Legislation

Crockett

Stoklas²,

O’Shea

et al. 2021

Studies in Computational Intelligence

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Adaptive Psychological Profiling systems use artificial intelligence algorithms to analyze a person's non-verbal behavior in order to determine a specific mental state such as deception. One such system known as, Silent Talker, combines image processing and artificial neural networks to classify multiple non-verbal signals mainly from the face during a verbal exchange i.e. interview, to produce an accurate and comprehensive time-based profile of a subject's psychological state. Artificial neural networks are typically black-box algorithms; hence, it is difficult to understand how the classification of a person's behaviour is obtained. The new European Data Protection Legislation (GDPR), states that individuals who are automatically profiled, have the right to an explanation of how the "machine" reached its decision and receive meaningful information on the logic involved in how that decision was reached. This is practically difficult from a technical perspective, whereas from a legal point of view, it remains unclear whether this is sufficient to safeguard the data subject's rights. This chapter is an extended version of a previous published paper in IJCCI 2019 [35] which examines the new European Data Protection Legislation and how it impacts on an application of psychological profiling within an Automated Deception Detection System (ADDS) which is one component of a smart border control system known as iBorderCtrl. ADDS detects deception through an avatar border guard interview, during a participants' pre-registration, to demonstrate the challenges faced in trying to obtain explainable decisions from models derived through computational intelligence techniques. The chapter concludes by examining the future of explainable decision making through proposing a new Hierarchy of Explainability and Empowerment that allows information and decision-making complexity to be explained at different levels depending on a person's abilities.

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Section: Results and Analysismentioning

confidence: 99%

Reconciling Adapted Psychological Profiling with the New European Data Protection Legislation

Crockett

Stoklas²,

O’Shea

et al. 2021

Studies in Computational Intelligence

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The politics of deceptive borders: ‘biomarkers of deceit’ and the case of iBorderCtrl

Sánchez‐Monedero

Dencik

2020

Information, Communication & Society

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This paper critically examines a recently developed proposal for a border control system called iBorderCtrl, designed to detect deception based on facial recognition technology and the measurement of micro-expressions, termed 'biomarkers of deceit'. Funded under the European Commission's Horizon 2020 programme, the system is analysed in relation to the wider political economy of 'emotional AI' and the history of deception detection technologies. We then move on to interrogate the design of iBorderCtrl using publicly available documents and assess the assumptions and scientific validation underpinning the project design. Finally, drawing on a Bayesian analysis we outline statistical fallacies in the foundational premise of mass screening and argue that it is very unlikely that the model that iBorderCtrl provides for deception detection would work in practice. By interrogating actual systems in this way, we argue that we can begin to question the very premise of the development of datadriven systems, and emotional AI and deception detection in particular, pushing back on the assumption that these systems are fulfilling the tasks they claim to be attending to and instead ask what function such projects carry out in the creation of subjects and management of populations. This function is not merely technical but, rather, we argue, distinctly political and forms part of a mode of governance increasingly shaping life opportunities and fundamental rights.

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An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification

et al. 2023

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Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable data structure is not transparent, it is critical to develop a specific data classification model from a new and unexpected dataset. In this paper, we propose a novel approach using the concepts of DNN and decision tree (DT) for classifying nonlinear data. We first developed a decision tree-based neural network (DTBNN) model. Next, we extend our model to a decision tree-based deep neural network (DTBDNN), in which the multiple hidden layers in DNN are utilized. Using DNN, the DTBDNN model achieved higher accuracy compared to the related and relevant approaches. Our proposal achieves the optimal trainable weights and bias to build an efficient model for nonlinear data classification by combining the benefits of DT and NN. By conducting in-depth performance evaluations, we demonstrate the effectiveness and feasibility of the proposal by achieving good accuracy over different datasets.

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A hybrid model combining neural networks and decision tree for comprehension detection

Cited by 3 publications

References 30 publications

Reconciling Adapted Psychological Profiling with the New European Data Protection Legislation

Reconciling Adapted Psychological Profiling with the New European Data Protection Legislation

The politics of deceptive borders: ‘biomarkers of deceit’ and the case of iBorderCtrl

An Advanced Decision Tree-Based Deep Neural Network in Nonlinear Data Classification

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