Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation

Ullah, Ihsan; Rios, Andre; Gala, Vaibhav; McKeever, Susan

doi:10.3390/app12010136

Cited by 23 publications

(10 citation statements)

References 53 publications

(78 reference statements)

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“…While hybrid models are powerful, their complicated structures make them difficult to interpret. Model predictions can be attributed back to input features using techniques such as Layer-wise Relevance Propagation (LRP) and Shapley Additive Explanations (SHAP) to facilitate explainable AI [100].…”

Section: 4 Discussion and Practical Hintsmentioning

confidence: 99%

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Chen,

Hussain,

Cauteruccio

et al. 2024

CMES

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Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and frameworks for financial time series prediction, with an emphasis on state-of-the-art deep learning models in the literature from 2015 to 2023, including standalone models like convolutional neural networks (CNN) that are capable of extracting spatial dependencies within data, and long short-term memory (LSTM) that is designed for handling temporal dependencies; and hybrid models integrating CNN, LSTM, attention mechanism (AM) and other techniques. For illustration and comparison purposes, models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input, output, feature extraction, prediction, and related processes. Among the state-of-the-art models, hybrid models like CNN-LSTM and CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model. Some remaining challenges have been discussed, including non-friendliness for finance domain experts, delayed prediction, domain knowledge negligence, lack of standards, and inability of real-time and highfrequency predictions. The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review, compare and summarize technologies and recent advances in this area, to facilitate smooth and informed implementation, and to highlight future research directions.

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Section: 4 Discussion and Practical Hintsmentioning

confidence: 99%

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Chen,

Hussain,

Cauteruccio

et al. 2024

CMES

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“…Despite its superior predictive performance, deep learning is often criticized for its poor model interpretability. To overcome this limitation, deep learning algorithms that focus on model explanations have emerged in recent years ( Lundberg and Lee, 2017 ; Ribeiro et al., 2016 ; Ullah et al., 2020 ).…”

Section: Machine Learning For Multi-omics Integrationmentioning

confidence: 99%

Machine learning for multi-omics data integration in cancer

et al. 2022

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“…SHAP presents a unified approach to model prediction. SHAP uses a combination of Local Interpretable Model agnostic exPlanations (LIME) [20], Deep Lift [21], Layer-wise Relevance Propagation [22], and Classic Shapely Value Estimation. Shapely Values are designed for Shapely Regression, Shapely Sampling, and Quantitative Input Influence on features using LIME, Deep Lift, and Layer-wise Relevance Propagation.…”

Section: Shapely Additive Explanations (Shap) [18] [19]mentioning

confidence: 99%

Application of Feature Engineering and Ensemble Techniques on Medical End-devices for Geriatric Fall Detection

Nandi,

Anupama,

Agarwal

et al. 2023

Preprint

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Falls, especially those left unattended, are fatal for the elderly. Several efforts have been made to use the Internet of Things and Machine Learning algorithms to detect falls. All such systems have issues such as (a) sensor placement on the torso and thigh which will be uncomfortable for the elderly. (b) Predictions made on the cloud- the result of the prediction is then sent back to the end device to raise an alert. This is prone to network connectivity and latency issues. We have built an end/device that is wrist-worn and has multiple Inertial Measurement Unit sensors and a heart sensor. We have developed three novel ensemble algorithms (a)Stack(A) (b) Variable weighted Ensemble Voting algorithm-B(VWE(B)), and (c) Variable weighted Ensemble Voting algorithm-C (VWE(C)). Since the ensemble algorithm is run on the end-device built around Qualcomm Snapdragons 820c, we do both feature extraction and selection to reduce data dimensionality. We have used multiple methods such as (a) Identifying features that have maximum impact (b) Principal Component Analysis (PCA) (c) Shapley’s values (d) Cross-Correlation combined with relliefF. We got an accuracy of 97% and specificity of 99%. In this paper, we present the analysis of the system with and without pruning.

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Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation

Cited by 23 publications

References 53 publications

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Machine learning for multi-omics data integration in cancer

Application of Feature Engineering and Ensemble Techniques on Medical End-devices for Geriatric Fall Detection

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