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Found 11 papers, 10 papers with code
Perfectly predicting ICU length of stay: too good to be true
A paper of Alsinglawi et al was recently accepted and published in Scientific Reports.
Powershap: A Power-full Shapley Feature Selection Method
Benchmarks and simulations show that powershap outperforms other filter methods with predictive performances on par with wrapper methods while being significantly faster, often even reaching half or a third of the execution time.
pyRDF2Vec: A Python Implementation and Extension of RDF2Vec
This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions.
R-GCN: The R Could Stand for Random
The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs).
Ranked #1 on
Node Classification
on MUTAG
MAGIC: Mining an Augmented Graph using INK, starting from a CSV
A large portion of structured data does not yet reap the benefits of the Semantic Web.
Ranked #5 on
Cell Entity Annotation
on BiodivTab
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
GENDIS: GENetic DIscovery of Shapelets
It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets.
Ranked #3 on
Outlier Detection
on ECG5000
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph
Deep-learning based techniques are increasingly being used for different machine learning tasks on knowledge graphs.
Ranked #3 on
Node Classification
on BGS
Positive blood culture detection in time series data using a BiLSTM network
The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions.
GENESIM: genetic extraction of a single, interpretable model
The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques.
