70,076 research outputs found
Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.
In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Endowing the robotic systems with cognitive capabilities for recognizing
daily activities of humans is an important challenge, which requires
sophisticated and novel approaches. Most of the proposed approaches explore
pattern recognition techniques which are generally based on hand-crafted
features or learned features. In this paper, a novel Hierarchal Multichannel
Deep Residual Network (HMResNet) model is proposed for robotic systems to
recognize daily human activities in the ambient environments. The introduced
model is comprised of multilevel fusion layers. The proposed Multichannel 1D
Deep Residual Network model is, at the features level, combined with a
Bottleneck MLP neural network to automatically extract robust features
regardless of the hardware configuration and, at the decision level, is fully
connected with an MLP neural network to recognize daily human activities.
Empirical experiments on real-world datasets and an online demonstration are
used for validating the proposed model. Results demonstrated that the proposed
model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
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