Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes

Abstract

Background. Multi-view data representation learning explores the relationship between the views and provides rich complementary information that can improve computer-aided diagnosis. Specifically, existing machine learning methods devised to automate neurological disorder diagnosis using brain data provided new insights into how a particular disorder such as autism spectrum disorder (ASD) alters the brain construct. However, the performance of machine learning methods highly depends on the size of the training samples from both classes. In a real-world clinical setting, such medical data is very expensive and challenging to collect, might (i) su↵er from several limitations such as imbalanced classes and (ii) have non-heterogeneous distribution when derived from multi-view brain representations.-New Method. To the best of our knowledge, the problem of imbalanced and multi-view data classification remains unexplored in the field of network neuroscience. To fill this gap, we propose a Multi-View LEArning-based data Proliferator (MV-LEAP) that enables the classification of imbalanced multiview representations. MV-LEAP comprises two key steps. First, a manifold learning-based proliferator, which enables to generate synthetic data for each view, is developed to handle imbalanced data. Second, a multi-view manifold data alignment leveraging tensor canonical correlation analysis is proposed to map all original and proliferated (i.e., synthesized) views into a shared subspace where their distributions are aligned for the target classification task.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

“…The present study complements a wide array of insightful investigations that have explored the pitfalls and potential solutions for supervised learning with imbalanced data [11,13,46,47,48,17,49,50,32,51,20,52,14,53]. By contrast to some of the previous studies, the present work focuses on insights that are directly relevant to researchers in neuroimaging using standard tools, such as those available through the scikit-learn library.…”

“…However, this comes at the cost of reducing the sample size, increasing the signal-to-noise ratio, which can be detrimental to the classification. Alternatively, one can oversample the minority class by duplicating or interpolating observations [17, 18, 19] (Fig. 1a), though this comes with a higher risk of overfitting and introducing noise [20].…”

Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

“…e diversified ensemble learning framework, which finds the best classification algorithm for each individual subdataset, is proposed in the literature [17,18]. Graa and Rekik [19] proposed a multiview learning-based data proliferator (MV-LEAP) that enables the classification of imbalanced multiview representations. Shi et al [20] proposed a general multiple distribution selection method for imbalanced data classification, by proving that traditional classification methods that use single softmax distribution are limited for modeling complex and imbalanced data.…”

A diagnostic unified classification model for classifying multi-sized and multi-modal brain graphs using graph alignment

“…However, this overlooks the complementarity and richness of multimodal connectomes and becomes more time-consuming as the number of modalities increases. Another solution is to combine different multimodal connectomes in the given population by simple concatenation and then train a single classifier using the concatenated feature vectors (Dai and He, 2014;Schouten et al, 2016;Raeper et al, 2018;Graa and Rekik, 2019;Corps and Rekik, 2019). However, such approach requires paired samples across modalities, i.e., all MRI modalities should be available for each subject in the population.…”