Semi-supervised regression: A recent review

Kostopoulos, Georgios; Karlos, Stamatis; Kotsiantis, Sotiris; Ragos, Omiros

doi:10.3233/jifs-169689

Cited by 129 publications

(59 citation statements)

References 36 publications

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“…I. If x l is a numerical attribute the sets L 0 l and U 0 l are passed in a multi-scheme semi-supervised regression (SSR) procedure [36]. This scheme utilizes three regression algorithms (hereinafter referred to as regressors) in order to efficiently augment L 0 l with the U 0 l instances [37].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Iterative Robust Semi-Supervised Missing Data Imputation

et al. 2020

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In many real-world applications scientists are often confronted with the problem of incomplete datasets due to several reasons. The direct analysis of datasets with missing values in attributes inevitably results in inaccurate learning models and erroneous results. Facing effectively the challenge of missing values is an essential step of the data mining process. Imputation is often employed to overcome the shortcomings incurred by missing data during the pre-process stage of data analysis. Therefore, a plethora of statistical and machine learning methods have been proposed and employed with a view to imputing the missing values in incomplete data with their potential or actual values. In this context, the main objective of this paper is to put forward an iterative stepwise imputation method based on the semi-supervised learning approach, called IRSSI. Semi-supervised methods have proved to be particularly effective for exploiting incomplete or partially labeled data with regard to the values of the target attribute. The proposed algorithm was experimentally evaluated on real-world benchmark datasets and artificially generated datasets using different high ratios of missing data. The experimental results demonstrate the efficiency of IRSSI algorithm compared to typical imputation methods.

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Section: The Proposed Methodsmentioning

confidence: 99%

Iterative Robust Semi-Supervised Missing Data Imputation

et al. 2020

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“…This requires the learning algorithm to generalize from the training data to unseen situations in a reasonable way. Standard supervised learning problem can be generalized as: (1) standard supervised learning, which includes algorithms such as support vector machine (SVM), naïve Bayes, decision trees, k-nearest neighbor, boosting, random forest, or artificial neural network (ANN) [28]; (2) semi-supervised learning, including algorithms such as artificial neural networks or graph-based methods [29]; and (3) structured prediction algorithms, such as Bayesian networks or random field [30].…”

Section: Related Workmentioning

confidence: 99%

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

Gajowniczek

Grzegorczyk

Ząbkowski

2019

Sensors

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The literature indicates that 90% of clinical alarms in intensive care units might be false. This high percentage negatively impacts both patients and clinical staff. In patients, false alarms significantly increase stress levels, which is especially dangerous for cardiac patients. In clinical staff, alarm overload might lead to desensitization and could result in true alarms being ignored. In this work, we applied the random forest method to reduce false arrhythmia alarms and specifically explored different methods of probability and class assignment, as these affect the classification accuracy of the ensemble classifiers. Due to the complex nature of the problem, i.e., five types of arrhythmia and several methods to determine probability and the alarm class, a synthetic measure based on the ranks was proposed. The novelty of this contribution is the design of a synthetic measure that helps to leverage classification results in an ensemble model that indicates a decision path leading to the best result in terms of the area under the curve (AUC) measure or the global accuracy (score). The results of the research are promising. The best performance in terms of the AUC was 100% accuracy for extreme tachycardia, whereas the poorest results were for ventricular tachycardia at 87%. Similarly, in terms of the accuracy, the best results were observed for extreme tachycardia (91%), whereas ventricular tachycardia alarms were the most difficult to detect, with an accuracy of only 51%.

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“…While more than 90% of genes in scRNA-seq data are zero-counts and the true and dropout zeros are difficult to distinguish, genes in each cell with detected expression (positive-count genes) are more reliable measurements compared to zeros (zero-count genes). Semi-supervised learning (SSL) approach offers promise when a few labels are available by allowing models to supplement their training with unlabeled data [17]. We hypothesize that SSL can build a reliable imputation algorithm by learning information from both positive-and zero-count genes, which can be treated as labeled and unlabeled data, respectively.…”

Section: Introductionmentioning

confidence: 99%

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning

Yuan

et al. 2020

Genome Biol

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Dropouts distort gene expression and misclassify cell types in single-cell transcriptome. Although imputation may improve gene expression and downstream analysis to some degree, it also inevitably introduces false signals. We develop DISC, a novel deep learning network with semi-supervised learning to infer gene structure and expression obscured by dropouts. Compared with seven state-of-the-art imputation approaches on ten real-world datasets, we show that DISC consistently outperforms the other approaches. Its applicability, scalability, and reliability make DISC a promising approach to recover gene expression, enhance gene and cell structures, and improve cell type identification for sparse scRNA-seq data.

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Semi-supervised regression: A recent review

Cited by 129 publications

References 36 publications

Iterative Robust Semi-Supervised Missing Data Imputation

Iterative Robust Semi-Supervised Missing Data Imputation

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning

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