[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Improving identification of difficult small classes by balancing class distribution
J Laurikkala - Artificial Intelligence in Medicine: 8th Conference on …, 2001 - Springer
We studied three methods to improve identification of difficult small classes by balancing
imbalanced class distribution with data reduction. The new method, neighborhood cleaning …
imbalanced class distribution with data reduction. The new method, neighborhood cleaning …
Efficient k-nearest neighbor search based on clustering and adaptive k values
Abstract The k-Nearest Neighbor (k NN) algorithm is widely used in the supervised learning
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
While standing as one of the most widely considered and successful supervised
classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor …
classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor …
The nearest subclass classifier: A compromise between the nearest mean and nearest neighbor classifier
CJ Veenman, MJT Reinders - IEEE Transactions on Pattern …, 2005 - ieeexplore.ieee.org
We present the nearest subclass classifier (NSC), which is a classification algorithm that
unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest …
unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest …
Improving kNN multi-label classification in Prototype Selection scenarios using class proposals
Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by
keeping only the most profitable prototypes of the training set. In turn, these schemes …
keeping only the most profitable prototypes of the training set. In turn, these schemes …
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
Class imbalance and class overlap are two of the major problems in data mining and
machine learning. Several studies have shown that these data complexities may affect the …
machine learning. Several studies have shown that these data complexities may affect the …
Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining
G Toussaint - International Journal of Computational Geometry & …, 2005 - World Scientific
In the typical nonparametric approach to classification in instance-based learning and data
mining, random data (the training set of patterns) are collected and used to design a …
mining, random data (the training set of patterns) are collected and used to design a …
A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm
T Raicharoen, C Lursinsap - Pattern recognition letters, 2005 - Elsevier
This paper presents a new method based on divide-and-conquer approach to the selection
and replacement of a set of prototypes from the training set for the nearest neighbor rule …
and replacement of a set of prototypes from the training set for the nearest neighbor rule …
Extensions to rank-based prototype selection in k-Nearest Neighbour classification
The k-nearest neighbour rule is commonly considered for classification tasks given its
straightforward implementation and good performance in many applications. However, its …
straightforward implementation and good performance in many applications. However, its …