Publicacoes - INESC TEC

Publicações

Publicações por Pavel Brazdil

2001

Preface

Autores
Brazdil, P; Jorge, A;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2005

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Autores
Jorge, A; Torgo, L; Brazdil, P; Camacho, R; Gama, J;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2018

Incremental Sparse TFIDF & Incremental Similarity with Bipartite Graphs

Autores
Sarmento, RP; Brazdil, P;

Publicação
CoRR

Abstract

2022

Contextualization for the Organization of Text Documents Streams

Autores
Sarmento, RP; Cardoso, DdO; Gama, J; Brazdil, P;

Publicação
CoRR

Abstract

2018

Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks

Autores
Cordeiro, M; Sarmento, RP; Brazdil, P; Gama, J;

Publicação
CoRR

Abstract

2022

On Usefulness of Outlier Elimination in Classification Tasks

Autores
Hetlerovic, D; Popelinsky, L; Brazdil, P; Soares, C; Freitas, F;

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022

Abstract
Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5%) on average.

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