Search Results for author:
Found 45 papers, 9 papers with code
Local overlap reduction procedure for dynamic ensemble selection
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class.
Dynamic Ensemble Selection Using Fuzzy Hyperboxes
Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample.
Classifier Pool Generation based on a Two-level Diversity Approach
This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions.
A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition
Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to digit segmentation, which is dominated by heuristics, thereby imposing substantial constraints on the final performance.
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation.
Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation
The method is used to generate offline signatures in the image and the feature space and train an ASVS.
Random Forest for Dissimilarity-based Multi-view Learning
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions.
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task.
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV).
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification
Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context.
Multi-label learning for dynamic model type recommendation
Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant model types based on the local data complexity of the region surrounding each test sample.
An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings
An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model.
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures.
A Decision-Based Dynamic Ensemble Selection Method for Concept Drift
We propose an online method for concept driftdetection based on dynamic classifier ensemble selection.
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning systems.
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems.
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach
In this paper, we propose improvements to the training and generalization phase of the META-DES framework.
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence.
Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample.
On Meta-Learning for Dynamic Ensemble Selection
The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance.
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class.
META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance.
Online local pool generation for dynamic classifier selection: an extended version
Thus, we propose in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space.
A writer-independent approach for offline signature verification using deep convolutional neural networks features
The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods.
Dynamic voting in multi-view learning for radiomics applications
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients.
Segmentation-Free Approaches for Handwritten Numeral String Recognition
This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length.
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
Experiments are performed on 18 state-of-the-art DS techniques over 30 classification datasets and results show that DS methods present a significant boost in classification accuracy even though they use the same neighborhood as the K-NN.
An Ensemble Generation Method Based on Instance Hardness
Techniques such as Bagging and Boosting have been successfully applied to a variety of problems.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats.
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Networks to learn representations from signature pixels.
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning.
Dissimilarity-based representation for radiomics applications
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
Deep Learning Architectures for Face Recognition in Video Surveillance
In video-based FR systems, facial models of target individuals are designed a priori during enrollment using a limited number of reference still images or video data.
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic selection techniques.
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it.
Analyzing features learned for Offline Signature Verification using Deep CNNs
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points.
Bayesian Hyperparameter Optimization for Ensemble Learning
Our method consists in building a fixed-size ensemble, optimizing the configuration of one classifier of the ensemble at each iteration of the hyperparameter optimization algorithm, taking into consideration the interaction with the other models when evaluating potential performances.
Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others.
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
In order to perform a more robust ensemble selection, we proposed the META-DES framework using meta-learning, where multiple criteria are encoded as meta-features and are passed down to a meta-classifier that is trained to estimate the competence level of a given classifier.
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem.
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation
Numerous genotypic diversity measures (GDMs) are available in the literature to assess the convergence status of an evolutionary algorithm (EA) or describe its search behavior.
Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results
An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic implementation that uses handwritten signature images as biometrics instead of traditional passwords to secure private cryptographic keys.
A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces
Our scheme is the first ensemble selection method to be presented in the literature based on the concept of data diversity.
