Name: Elaine Cecília Gatto
Type: User
Bio: Data Scientist, Machine Learning Researcher, Computer Engineer, Computer Scientist, Professor, Researcher, Speaker, Writer, Singer, Geek, Nerd, Otaku, Gamer
Twitter: cissagatto
Location: Jaú
Blog: https://sites.google.com/view/cissagatto
Elaine Cecília Gatto's Projects
This code generates partitions based on bell numbers for multilabel classification.
This code is part of my doctoral research. The aim choose the best partition generated.
This code is part of my doctoral research. The aim choose the best partition generated.
This code is part of my PhD research. This code select the best partition using the CLUS framework. We choose the partition with the best Macro-F1.
This code is part of my Ph.D. research. This code selects the best partition using the CLUS framework. We choose the partition with the best Micro-F1.
This code is part of my PhD research. This code select the best partition using the silhouete coefficient.
Repository of the paper "Community Detection Methods for Multi-Label Classification" publish in BRACIS 2023
Building a container with APPTAINER to run R scripts on a cluster with SLURM.
This code is part of my doctoral research. The aim is test the best hybrid partitions chosen with silhouette coefficient. But here we using a chain of hybrid partitions to do the test.
This repository contains the concepts, equations and commands to do statistical analysis with R and Python
Cheat Sheet Pipeline MIPS 32 Bits
A modified version of Clus System for Hierarchical Multi-Label Classification.
A code to execute and save cross-validation in multilabel classification
Repositório para as aulas de Git do BootCamp de Data Analytics da WoMakersCode que estou participando!
This code is part of my Ph.D. research. The R script runs in parallel the ECC made in python.
This code is part of my doctoral research. The aim is to build, validate and test all possible partitions for multilabel classification using CLUS framework.
This code is part of my doctoral research. The aim is to build, validate and test all possible partitions for multilabel classification using CLUS framework.
Generates hybrid partitions using community detection methods.
This code is part of my doctoral research. The aim is to generate partitions from the Jaccard index for multilabel classification.
This code is part of my PhD research. This code generate hybrid partitions using Kohonen to modeling the labels correlations, and HClust to partitioning the label space.
This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.
This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.
This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.
This code is part of my doctoral research. The aim is to generate partitions using Rogers-Tanimoto similarity measure.
This code is part of my PhD project. The aim is build dendrograms from similarity matrices and chose the best one. Then, the best dendrogram is cutted in all possible levels producing the desired Hybrid Partitions.
:earth_americas: Extra Coordinate Systems, Geoms, Statistical Transformations & Scales for 'ggplot2'