Supervised learning algorithms for multi-class classification problems with partial class memberships

Waegeman, Willem; Verwaeren, Jan; Slabbinck, Bram; Baets, Bernard De

doi:10.1016/j.fss.2010.11.012

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…In the current QPCR dataset, we have samples from three different groups: controls, BPS and DO, making it a multi-class classification problem. While most ML algorithms were originally designed for binary classification [10], they can be adapted for multi-class problems using strategies like One-vs-Rest and One-vs-One. In our case, we employed the One-vs-One technique, fitting one binary classification model for each pair of classes (BPS vs Control, DO vs Control, or BPS vs DO).…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome

Akshay,

Besic,

Kuhn

et al. 2024

Preprint

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Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non- ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)- based approach for the identification of mRNA signatures specific to non-ulcerative BPS.Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was subsequently validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. Notably, a three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier, effectively distinguishing patients with non- ulcerative BPS from controls and patients with DO. This signature was universally selected by both supervised and unsupervised approaches.The ML-based framework used to define BPS classifiers not only establishes a solid foundation for comprehending the specific gene expression changes in the bladder of the patients with BPS but also serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

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Section: Supervised Machine Learningmentioning

confidence: 99%

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome

Akshay,

Besic,

Kuhn

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…We expect that far better heuristics can be produced if we can encode prior knowledge about the classification problem into it, as is often possible in practical problems. Heuristics could also be designed for other variations of unsupervised classification, such as learning with partial class memberships, for example [25].…”

Section: Runtimementioning

confidence: 99%

Unsupervised Multi-class Regularized Least-Squares Classification

Pahikkala

Airola

Gieseke

et al. 2012

2012 IEEE 12th International Conference on Data Mining

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Regularized least-squares classification is one of the most promising alternatives to standard support vector machines, with the desirable property of closed-form solutions that can be obtained analytically, and efficiently. While the supervised, and mostly binary case has received tremendous attention in recent years, unsupervised multi-class settings have not yet been considered. In this work we present an efficient implementation for the unsupervised extension of the multi-class regularized least-squares classification framework, which is, to the best of the authors' knowledge, the first one in the literature addressing this task. The resulting kernel-based framework efficiently combines steepest descent strategies with powerful meta-heuristics for avoiding local minima. The computational efficiency of the overall approach is ensured through the application of matrix algebra shortcuts that render efficient updates of the intermediate candidate solutions possible. Our experimental evaluation indicates the potential of the novel method, and demonstrates its superior clustering performance over a variety of competing methods on real-world data sets.

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“…The possibility of alternative prediction combiner in the ensemble for multiclass classification problem can be formulated [31,32]. Instead of giving one label in the final classification, two class labels with high weighted voting represented as probabilities are combined as an OR-tree combiner.…”

Section: Introductionmentioning

confidence: 99%

“…Among nearest examples we can find on this specific problem are [31][32][33]. Work by [12] is perhaps the most current ensemble that utilizing the combination of NB and kNN.…”

Section: Introductionmentioning

confidence: 99%

A Direct Ensemble Classifier for Imbalanced Multiclass Learning

Sainin

Alfred

2012

2012 4th Conference on Data Mining and Optimization (DMO)

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Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks. Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain. Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers. In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers. The DECIML framework has been tested with several benchmark dataset and shows promising results.

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Supervised learning algorithms for multi-class classification problems with partial class memberships

Cited by 15 publications

References 27 publications

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome

Unsupervised Multi-class Regularized Least-Squares Classification

A Direct Ensemble Classifier for Imbalanced Multiclass Learning

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