PIP: Pictorial Interpretable Prototype Learning for Time Series Classification [0.03%]
时间序列分类的图解可解释原型学习（PIP）
Alireza Ghods,Diane J Cook
Alireza Ghods
Time series classifiers are not only challenging to design, but they are also notoriously difficult to deploy for critical applications because end users may not understand or trust black-box models. Despite new efforts, explanations genera...
Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder [0.03%]
多组学增强医师评估可提高药物反应预测性：抑郁症病例研究
Arjun Athreya,Ravishankar Iyer,Drew Neavin et al.
Arjun Athreya et al.
This work proposes a "learning-augmented clinical assessment" workflow to sequentially augment physician assessments of patients' symptoms and their socio-demographic measures with heterogeneous biological measures to accurately predict tre...
An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems [0.03%]
一种统计学和可视化引导的知识发现分析流水线用于密歇根风格的学习分类器系统
Ryan J Urbanowicz,Ambrose Granizo-Mackenzie,Jason H Moore
Ryan J Urbanowicz
Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real worl...