The Strength of Weak Learnability
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Induction of Decision Trees
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental learning with partial instance memory
Artificial Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A note on the utility of incremental learning
AI Communications
Computational Statistics & Data Analysis
The class imbalance problem: A systematic study
Intelligent Data Analysis
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis
Applied Intelligence
Ranking of Brain Tumour Classifiers Using a Bayesian Approach
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Writer Adaptive Online Handwriting Recognition Using Incremental Linear Discriminant Analysis
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
IEEE Transactions on Neural Networks
Avoiding order effects in incremental learning
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental knowledge acquisition in supervised learning networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Parameter Incremental Learning Algorithm for Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.