Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts.
Rui DingJianguo LiuKang HuaXuebin WangXiaoben ZhangMinhua ShaoYuxin ChenJunhong ChenPublished in: CoRR (2024)
Keyphrases
- multistage
- domain adaptation
- active learning
- machine learning
- efficient discovery
- data mining
- labeled data
- transfer learning
- semi supervised
- semi supervised learning
- unlabeled data
- covariate shift
- target domain
- association rules
- knowledge discovery
- supervised learning
- dynamic programming
- learning algorithm
- learning tasks
- text mining
- cross domain
- co training
- multiple sources
- machine learning algorithms
- text classification
- data analysis
- decision trees
- transactional databases
- optimal policy
- knowledge transfer
- text categorization
- support vector machine
- information extraction
- data mining techniques
- model selection
- itemsets
- document classification
- reinforcement learning
- test data
- natural language processing
- sentiment classification
- training examples
- feature selection
- similarity measure
- spatial data mining
- spatial datasets
- databases
- data sets