Semi-supervised learning approaches with applications in Medicinal Chemistry

Semi-supervised learning with a graph-based approach has gained prominence in machine learning, particularly in scenarios where labeling data involves substantial costs. Graph convolution networks (GCNs) have found widespread application in semi-supervised learning, predominantly on graph-structured data such as citation and social networks. However, a noticeable gap exists in the application of these methods to non-graph multi-view data, such as collections of images. In an effort to address this gap, we introduce two innovative deep semi-supervised multi-view classification models specifically tailored for non-graph data. Both models share a common architecture, leveraging GCNs and integrating a label smoothing constraint. The primary distinction lies in the construction of the consensus similarity graph. The first model directly reconstructs the consensus graph from different views using a specialized objective function designed for flexible graph-based semi-supervised classification. In contrast, the second model independently reconstructs individual graphs and subsequently adaptively merges them into a unified consensus graph. Our experiments encompass various multiple-view image datasets. The results consistently demonstrate the superior performance of our proposed approach compared to traditional fusion methods with GCNs. In this research, we present two approaches for tackling semi-supervised classification challenges involving multiple views. One method is named Semi-supervised Classification with a Unified Graph (SCUG), and the other is referred to as Semi-supervised Classification with a Fused Graph (SC-Fused). Both methods share a common semi-supervised classification process, utilizing the GCN framework and incorporating label smoothing. However, the key distinction lies in the construction of the similarity graph. Unlike traditional ad hoc graph construction approaches, our proposed methods, SCUG and SC-Fused, estimate the unified graph or individual graphs, respectively, alongside the labels. This results in more optimized graphs that benefit from data smoothing and the semi-supervised context.

A semi-supervised deep learning approach for cropped image detection

In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution data; yet on the other hand, the process of obtaining the ground truth data may be cumbersome for such large repositories. In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem. To that extend, we consider two basic unsupervised approaches by enlarging the initial labeled training set as well as an ensemble-based self-training method. We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance of the underlying classifier ensemble. We evaluate the effectiveness of the proposed semi-supervised learning approach over polarimetric SAR data. Results show that the proposed self-training approach using an ensemble-based classifier that is initially trained over a small training set can achieve a similar performance level of a fully supervised learning approach where the training is performed over significantly larger labeled data. Considering the difficulties of the manual data labeling in such massive volumes of SAR repositories, this is indeed a promising accomplishment for semi-supervised SAR classification.

The problem of utilizing machine learning approach in Indonesian Named Entity Recognition (NER) system is the limited amount of labelled data for training process. However, unlike the limited availability of labelled data, unlabelled data is widely available from many sources. This enables a semi-supervised learning approach to solve this NER system problem. This research aims to design a semi-supervised learning model to solve NER system problem. A semi-supervised co-training learning is used to utilize unlabelled data in NER learning process to produce new labelled data that can be applied to enhance a new NER classification system. This research uses two kinds of data, Indonesian DBPedia data as labelled data and news article text from Indonesian news sites (kompas.com, cnnindonesia.com, tempo.co, merdeka.com and viva.co.id) as unlabelled data. The pre-processing steps applied to analyze unstructured text are sentence segmentation, tokenization, stemming, and PoS Tagging. The results of this pre-process are the NER and its context used as unlabelled data for the semi-supervised co-training process. The SVM algorithm is used as a classi□cation algorithm in this process. 10 Cross Fold Validation is used as the system testing approach. Based on the result of the NER testing system, the precision is 73.6%, the recall is 80.1% and f1 mean is 76.5%.

The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary classification deciding whether two records should be classified as the same entity. The classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and classification. The choice of binary classification techniques in dependence of project specifications in particular data quality has not extensively been studied yet. The aim of this work is the introduction and evaluation of an automatable semi-supervised binary classification system applied within the field of record linkage capable of competing or even surpassing advanced automated techniques of the domain of unsupervised classification. This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary classification system and the comparison of its classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics. While the classification performance for both methods measured as F-measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised classification, 0.993 for unsupervised classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised classification and 0.803 for unsupervised classification. Aside from supplying a viable model for semi-supervised classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised techniques might generally be capable of outperforming unsupervised techniques especially on data with lower levels of data quality.

Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.

In this paper, the application of semi-supervised manifold learning techniques to the task of verifying hypothesized occurrences of spoken terms is investigated. These techniques are applied in a two stage spoken term detection framework where ASR lattices are first generated using a large vocabulary ASR system and hypothesized occurrences of spoken query terms in the lattices are verified in a second stage. The verification process is performed using a fixed dimensional feature representation derived from each hypothesized term occurrence. Two semi-supervised approaches namely, manifold regularized least squares (RLS) classification and spectral clustering, are investigated for distinguishing correct hypotheses from false alarms. It is shown that, exploiting unlabeled data in addition to labeled data using semi-supervised approaches, significantly improves the verification performance compared to the case where only the labeled data is used. This improvement in performance increases as the ratio of unlabeled to labeled data augments. It is also shown that, when training data is very limited, a comparable verification performance can be gained by exploiting only the acoustic similarity between the test samples using the spectral clustering approach.

Sample-weighted fused graph-based semi-supervised learning on multi-view data

High-dimensional semi-supervised learning via a fusion-refinement procedure

Due to the large number of malicious software (malware) and the large variety among them, automated detection and analysis using machine learning techniques have become more and moreimportant for network and computer security.An often encountered scenario in these security applications is that training examples are scarce but unlabeled data are abundant. Semi-supervised learning where both labeled and unlabeled data are used to learn a good model quickly is a natural choice under such condition.We investigate semi-supervised classification for malware categorization.We observed that malware data have specific characteristics and that they are noisy. Off-the-shelf semi-supervised learning may not work well in this case. We proposed a semi supervised approach that addresses the problems with malware data and can provide better classification. We conducted a set of experiments to test and compare our method to others. The experimental results show that semi-supervised classification is a promising direction for malware classification. Our method achieved more than 90% accuracy when there were only a few number of training examples. The results also indicates that modifications are needed to make semi-supervised learning work with malware data. Otherwise, semi-supervised classification may perform worse than classifiers trained on only the labeled data.

Semi-Supervised Machine Learning and Adaptive Data Clustering Approach for Software Defect Prediction

Automatic classification of construction safety reports using semi-supervised YAKE-Guided LDA approach

Semi-supervised learning has shown its potential in many real-world applications where only few labeled examples are available. However, when some fairness constraints need to be satisfied, semi-supervised classification models often struggle as they are required to cope with the lack of sufficient information for predicting the target variable while forgetting its relationships with any sensitive and potentially discriminatory attribute. To address this issue, we propose a fair semi-supervised representation learning architecture that leads to fair and accurate classification results even in very challenging scenarios with few labeled (but biased) instances. We show experimentally that our model can be easily adopted in very general settings, as the learned representations may be employed to train any supervised classifier. Moreover, when applied to several synthetic and real-world datasets, our method is competitive with state-of-the-art fair semi-supervised approaches.

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data. The COUGHVID dataset enlisted expert physicians to annotate and diagnose the underlying diseases present in a limited number of recordings. However, this approach suffers from potential cough mislabeling, as well as disagreement between experts.Methods: In this work, we use a semi-supervised learning (SSL) approach – based on audio signal processing tools and interpretable machine learning models – to improve the labeling consistency of the COUGHVID dataset for 1) COVID-19 versus healthy cough sound classification 2) distinguishing wet from dry coughs, and 3) assessing cough severity. First, we leverage SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and label sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classifiers.Results: The consistency of the re-labeled COVID-19 and healthy data is demonstrated in that it exhibits a high degree of inter-class feature separability: 3x higher than that of the user-labeled data. Similarly, the SSL method increases this separability by 11.3x for cough type and 5.1x for severity classifications. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs in the 1-1.5 kHz range (p=1.2×10−64), which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a COVID-19 classifier, achieving an AUC of 0.797.Conclusions: We propose a SSL expert knowledge aggregation technique for the field of cough sound classification for the first time, and demonstrate how it can be used to combine the medical knowledge of multiple experts in an explainable fashion, thus providing abundant, consistent data for cough classification tasks.

We investigate the important clinical problem of predicting prognosis-related breast cancer molecular subtypes using whole-transcriptome information present in The Cancer Genome Atlas Project (TCGA) dataset. From a Machine Learning perspective, the data is both high-dimensional with over nineteen thousand features, and extremely small with only about one thousand labeled instances in total. To deal with the dearth of information we compare classical, deep and semi-supervised learning approaches on the subtyping task. Specifically, we compare a L\(_1\)-regularized Logistic Regression, a 2-hidden layer Feed Forward Neural Network and a Variational Autoencoder based semi-supervised learner that makes use of pan-cancer TCGA data as well as normal breast tissue data from a second source. We find that the classical supervised technique performs at least as well as the deep and semi-supervised learning approaches, although learning curve analysis suggests that insufficient unlabeled data may be being provided for the chosen semi-supervised learning technique to be effective.