[BOOK][B] Combining pattern classifiers: methods and algorithms
LI Kuncheva - 2014 - books.google.com
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …
pattern recognition to ensemble feature selection, now in its second edition The art and …
[BOOK][B] Statistical pattern recognition
AR Webb - 2003 - books.google.com
Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …
many advances in recent years. New andemerging applications-such as data mining, web …
Imbalanced classification methods for student grade prediction: a systematic literature review
Student success is essential for improving the higher education system student outcome.
One way to measure student success is by predicting students' performance based on their …
One way to measure student success is by predicting students' performance based on their …
[HTML][HTML] A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data
The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …
classification algorithms usually assume that the number of samples in each class is similar …
Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition
Prototype learning is effective in improving the classification performance of nearest-
neighbor (NN) classifier and in reducing the storage and computation requirements. This …
neighbor (NN) classifier and in reducing the storage and computation requirements. This …
[BOOK][B] Fuzzy classifier design
L Kuncheva - 2000 - books.google.com
Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever
since have been a center of many discussions, fervently admired and condemned. Both …
since have been a center of many discussions, fervently admired and condemned. Both …
A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis
F Yang, K Wang, L Sun, M Zhai, J Song… - BMC Medical Informatics …, 2022 - Springer
Background Clinical diagnosis based on machine learning usually uses case samples as
training samples, and uses machine learning to construct disease prediction models …
training samples, and uses machine learning to construct disease prediction models …
Analysis of new techniques to obtain quality training sets
This paper presents new algorithms to identify and eliminate mislabelled, noisy and atypical
training samples for supervised learning and more specifically, for nearest neighbour …
training samples for supervised learning and more specifically, for nearest neighbour …
Learning weighted metrics to minimize nearest-neighbor classification error
In order to optimize the accuracy of the nearest-neighbor classification rule, a weighted
distance is proposed, along with algorithms to automatically learn the corresponding …
distance is proposed, along with algorithms to automatically learn the corresponding …
Editing Training Data for kNN Classifiers with Neural Network Ensemble
Y Jiang, ZH Zhou - International symposium on neural networks, 2004 - Springer
Since k NN classifiers are sensitive to outliers and noise contained in the training data set,
many approaches have been proposed to edit the training data so that the performance of …
many approaches have been proposed to edit the training data so that the performance of …