Titlebook: Machine Learning and Knowledge Extraction; 5th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2021

grenade

Fair and Adequate Explanations,artiality of counterfactual explanations that can hide biases; we define fair and adequate explanations in such a setting. We then provide formal results about the algorithmic complexity of fair and adequate explanations.

abnegate

Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamplpproximate any non-Markovian bounded reward function (with infinitely many reward values) with arbitrary precision. We also provide a lower bound for the episode length such that the proposed RL approach almost surely converges to an optimal policy in the limit. We test this approach on two RL envir

Irrepressible

Airbnb Price Prediction Using Machine Learning and Sentiment Analysis,so used to select the most representative features for predicting the price of the listings. Experimentation shows that SVR model can achieve an . score of 69% and a MSE of 0.147 (defined on ln(price)) on the test set, outperforming the other models considered in the paper. [Link to the repository:

subordinate

死亡

Deep Convolutional Neural Network (CNN) Design for Pathology Detection of COVID-19 in Chest X-Ray Isubsequently the images were used for training. After consolidation, COVID-19 images comprised only 5% of the dataset. To address the class imbalance, we have used dynamic image augmentation technique to reduce the bias. We have then explored custom CNN and VGG-16, InceptionNet-V3, MobileNet-V2, Res

閑聊

Meander

On the Trustworthiness of Tree Ensemble Explainability Methods,he accuracy and stability of global feature importance methods through comprehensive experiments done on simulations as well as four real-world datasets. We focus on tree-based ensemble methods as they are used widely in industry and measure the accuracy and stability of explanations under two scena

minaret

音樂學(xué)者

Mining Causal Hypotheses in Categorical Time Series by Iterating on Binary Correlations,ficantly high correlations are then combined and can be validated for causalities by means of plausibility and semantic criteria. Our experimental results are presented on anonymised real production state time series and a simple representational concept for further causal interpretation is introduced.

煩擾

Rice Seed Image-to-Image Translation Using Generative Adversarial Networks to Improve Weedy Rice Im from a typical mobile phone cameras into the closed environment setting. Our GAN architecture can translate mobile phone images and achieves 90.06% weedy rice recognition accuracy, as compared to 58.10% without the translation.