Intelligent Systems
9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part II
Page
%P
-
Article
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms
Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperpar...
-
Article
A systematic literature review on AutoML for multi-target learning tasks
Automated machine learning (AutoML) aims to automate machine learning (ML) tasks, eliminating human intervention from the learning process as much as possible. However, most studies on AutoML are related to un...
-
Article
Multi-label classification via closed frequent labelsets and label taxonomies
Multi-label classification (MLC) is a very explored field in recent years. The most common approaches that deal with MLC problems are classified into two groups: (i) problem transformation which aims to adapt ...
-
Chapter and Conference Paper
Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design
Constructive Machine Learning (CML) is a research field that uses algorithms to generate new instances, similar but not identical to existing ones. It has been widely used to assist the discovery of new drug-l...
-
Chapter and Conference Paper
Community Detection for Multi-label Classification
Exploring label correlations is one of the main challenges in multi-label classification. The literature shows that prediction performances can be improved when classifiers learn these correlations. On the oth...
-
Chapter and Conference Paper
AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification
Automated Machine Learning (AutoML) has achieved high popularity in recent years. However, most of these studies have investigated alternatives to single-label classification problems, presenting a need for mo...
-
Article
Open AccessInvestigating deep feedforward neural networks for classification of transposon-derived piRNAs
PIWI-interacting RNAs (piRNAS) form an important class of non-coding RNAs that play a key role in gene expression regulation and genome integrity by silencing transposable elements. However, despite the import...
-
Chapter and Conference Paper
Feature Selection for Hierarchical Multi-label Classification
In this work we study how conventional feature selection methods can be applied to Hierarchical Multi-label Classification Problems. In Hierarchical Multi-label Classification, instances can belong to two or m...
-
Chapter and Conference Paper
Predictive Bi-clustering Trees for Hierarchical Multi-label Classification
In the recent literature on multi-label classification, a lot of attention is given to methods that exploit label dependencies. Most of these methods assume that the dependencies are static over the entire ins...
-
Article
Active learning for hierarchical multi-label classification
Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in orde...
-
Book and Conference Proceedings
-
Book and Conference Proceedings
Intelligent Systems
9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I
-
Article
Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach
Multi-target regression (MTR) regards predictive problems with multiple numerical targets. To solve this, machine learning techniques can model solutions treating each target as a separated problem based only ...
-
Chapter and Conference Paper
Hierarchical Classification of Transposable Elements with a Weighted Genetic Algorithm
Most of the related works in Machine Learning (ML) are concerned ...
-
Article
Open AccessReduction strategies for hierarchical multi-label classification in protein function prediction
Hierarchical Multi-Label Classification is a classification task where the classes to be predicted are hierarchically organized. Each instance can be assigned to classes belonging to more than one path in the ...
-
Chapter and Conference Paper
Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions
Hierarchical Multi-Label Classification is a complex classification problem where the classes are hierarchically structured. This task is very common in protein function prediction, where each protein can have...
-
Chapter and Conference Paper
Hierarchical Multilabel Protein Function Prediction Using Local Neural Networks
Protein function predictions are usually treated as classification problems where each function is regarded as a class label. However, different from conventional classification problems, they have some specif...
-
Chapter and Conference Paper
Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques
Multilabel classification is an important problem in bioinformatics and Machine Learning. In a conventional classification problem, examples belong to just one among many classes. When an example can simultane...
