A survey on the explainability of supervised machine learning
N Burkart, MF Huber - Journal of Artificial Intelligence Research, 2021 - jair.org
Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …
often perceive the models as black boxes. Insights about the decision making are mostly …
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …
The coming of age of interpretable and explainable machine learning models
Abstract Machine-learning-based systems are now part of a wide array of real-world
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
[책][B] Credit scoring and its applications
L Thomas, J Crook, D Edelman - 2017 - SIAM
Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
Applying data mining techniques to e-learning problems
This chapter aims to provide an up-to-date snapshot of the current state of research and
applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still …
applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still …
Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis
J Yu, G Liu - Knowledge-Based Systems, 2020 - Elsevier
Deep neural network (DNN) with a complex structure and multiple nonlinear processing
units has achieved great success for feature learning in machinery fault diagnosis. Due to …
units has achieved great success for feature learning in machinery fault diagnosis. Due to …
Two-stage intrusion detection system in intelligent transportation systems using rule extraction methods from deep neural networks
In recent years, intrusion detection systems (IDSs) are offering effective solutions to protect
various types of cyber-attacks in different networks such as Internet of Vehicles (IoVs) …
various types of cyber-attacks in different networks such as Internet of Vehicles (IoVs) …
Reverse engineering the neural networks for rule extraction in classification problems
MG Augasta, T Kathirvalavakumar - Neural processing letters, 2012 - Springer
Artificial neural networks often achieve high classification accuracy rates, but they are
considered as black boxes due to their lack of explanation capability. This paper proposes …
considered as black boxes due to their lack of explanation capability. This paper proposes …
Deep logic networks: Inserting and extracting knowledge from deep belief networks
SN Tran, ASA Garcez - IEEE transactions on neural networks …, 2016 - ieeexplore.ieee.org
Developments in deep learning have seen the use of layerwise unsupervised learning
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …