119 citations · 235 across the 9 of their papers we have counts for
5 papers · 1 filter
highway2vec -- representing OpenStreetMap microregions with respect to their road network characteristics
Kacper Leśniara, Piotr Szymański
Recent years brought advancements in using neural networks for representation learning of various language or visual phenomena. New methods freed data scientists from hand-crafting…
LNEMLC: Label Network Embeddings for Multi-Label Classification
Piotr Szymański, Tomasz Kajdanowicz, Nitesh Chawla
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformat…
Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?
Piotr Szymański, Tomasz Kajdanowicz
We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier. Experimen…
A scikit-based Python environment for performing multi-label classification
Piotr Szymański, Tomasz Kajdanowicz
scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal…
How is a data-driven approach better than random choice in label space division for multi-label classification?
Piotr Szymański, Tomasz Kajdanowicz, Kristian Kersting
We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to r…