Circuit Performance Classification With Active Learning Guided Sampling for Support Vector Machines

Active Learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques.

Active learning approaches require a lot of time investment in student activities and engagement during the class period, which often leads to incomplete coverage of the course syllabus. Furthermore, it requires significant amount of time for the instructor to design and implement active learning strategies. All these shortcomings are often cited as the common hindrance in adopting student active learning. To address these widely recognized inhibiting factors, we recently implemented a new student active learning approach. This approach is based on the students’ presentation and hence termed as student presentation-based active learning (SPAL). Under this approach, students are given a reading assignment to prepare a PowerPoint presentation on well-defined conceptual topics, questions, or chapter modules. Reading assignments on a topic are administered 1–2 weeks before covering them in the class. This allows reasonable time for the self-comprehension of the suggested material for presentation preparation. Students were expected to rehearse the presentation and be prepared to complete it in the suggested time duration. During each lecture, one group of student would present the assigned topic to the class, and their presentations were graded according to the rubric focusing on the coverage of suggested topics, quality of presentation, and after presentation discussions. Peers and instructor provided feedback about the students’ presentation and unclear concepts. To understand the efficacy of this approach, this approach was implemented in Mechanical Engineering Senior Capstone Project I course in Fall 2016. Here the responses and insights garnered from this practice were presented, and discussion on the advantages and challenges associated with the adaptation of this approach in teaching engineering courses as compared to lecture based classroom education system. This paper can provide useful insights for instructors considering this approach or similar student active teaching approach in their courses.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

Recent developments in the field of Information Technology call for a specific reconsideration of the traditional educational methods and facilitate development of new teaching and learning methods and tools, opening it for a wider scope of concerned persons. Active teaching and learning methods as part of educational methodology are widely used at the universities across the European community, putting the larger responsibility of learning at the hands of the learners themselves. One of the major challenges is effective addressing the learning needs of the new generation of students. Postgraduate education in in the field of logistics and supply chain management (LSCM) must keep it in mind in order to promote a suitable answer to the LSCM labor market needs. This is accomplished well by a master programme in LSCM with a European dimension emerged from the synergy of international cooperation between Academia and the professional bodies. This paper shares the empirical study on implementation of Master study programme of Logistics and Supply Chain Management in the Riga Technical University and extensive use of active and innovative learning approach.

Active learning is one form of supervised machine learning. In supervised learning, a set of labeled samples is passed to a learning algorithm for training a classifier. However, labeling large amounts of training samples can be costly and error-prone. Active learning deals with the development of algorithms that interactively select a subset of the available unlabeled samples for labeling, and aims at minimizing the labeling effort while maintaining classification performance. The key challenge for the development of so-called active learning strategies is the balance between exploitation and exploration: On the one hand, the estimated decision boundary needs to be refined in feature space regions where it has already been established, while, on the other hand, the feature space needs to be scanned carefully for unexpected class distributions. In this thesis, two approaches to active learning are presented that consider these two aspects in a novel way. In order to lay the foundations for the first one, it is proposed to express the uncertainty in class prediction of a classifier at a test point in terms of a second-order distribution. The mean of this distribution corresponds to the common estimate of the posterior class probabilities and thus is related to the distance of the test point to the decision boundary, whereas the spread of the distribution indicates the degree of exploration in the corresponding region of feature space. This allows for the evaluation of the utility of labeling a yet unlabeled point with respect to classifier improvement in a principled way and leads to a completely novel approach to active learning. The proposed strategy is then implemented and evaluated based on kernel density classification. The generic active learning strategy can be combined with any other classifier, but it performs best if the derived second-order distributions are sufficiently good approximations to the sampling distribution. Although second-order distributions for random forests are derived in this thesis, they do not approximate sufficiently well the sampling distribution and mainly allow only for the relative comparison of prediction uncertainty between test points. In order to combine the state of the art classification performance of random forests with the principal ideas of the first active learning approach, a related second approach for random forests is derived. It is, in addition, tailored to the demands in industrial optical inspection: bag-wise labeling with weak labels and strongly imbalanced classes. Moreover, an outlier detection scheme based on random forests is derived that is used by the proposed active learning algorithm. Finally, a new computational scheme for Gaussian process classification is presented. It is compared to two standard methods in geostatistics, both with respect to theoretical consistency and practical performance. The method evolved as a by-product when considering using Gaussian process models for active learning.

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.

There is growing interest in the application of machine learning techniques in bioinformatics. The supervised machine learning approach has been widely applied to bioinformatics and gained a lot of success in this research area. With this learning approach researchers first develop a large training set, which is a time-consuming and costly process. Moreover, the proportion of the positive examples and negative examples in the training set may not represent the real-world data distribution, which causes concept drift. Active learning avoids these problems. Unlike most conventional learning methods where the training set used to derive the model remains static, the classifier can actively choose the training data and the size of training set increases. We introduced an algorithm for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer, lung cancer, and prostate cancer samples. We compared the classification performance of active learning with that of passive learning. The results showed that employing the active learning method can achieve high accuracy and significantly reduce the need for labeled training instances. For lung cancer classification, to achieve 96% of the total positives, only 31 labeled examples were needed in active learning whereas in passive learning 174 labeled examples were required. That meant over 82% reduction was realized by active learning. In active learning the areas under the receiver operating characteristic (ROC) curves were over 0.81, while in passive learning the areas under the ROC curves were below 0.50.

This letter presents a new committee-based active learning (AL) model that queries the contention samples between spectral–spatial-description-based classifier-patch-based support vector machine (PTSVM) and spectral-description-based classifier-pixel-based support vector machine (PXSVM) for classification of hyperspectral remote sensing images. The proposed model consists of three main steps. Firstly, a given image is partitioned into overlapping patches. Then, a PTSVM classifier and a PXSVM classifier are trained using the initial patch and corresponding pixel training sets, respectively. Secondly, a set of unlabelled pixels from pixel candidate pool whose prediction labels disagree by two classifiers are added to the contention pool (CTP). Lastly, a margin sampling (MS)-based AL method is employed to select the most informative pixels from CTP. These pixels are labelled by annotator and added to the pixel training set. At the same time, the patches that contain at least one of these pixels will be added to the patch training set. This process will be repeated until a predefined convergence condition is satisfied. Experimental results show good performance on two hyperspectral data-sets as compared to the state-of-the-art MS and entropy query-by-bagging-based AL models.

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries.

Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

We compare the level of accuracy achieved by active learning and domain adaptation approaches for quality of transmission estimation of an unestablished lightpath, in presence of small-sized training datasets.

In machine learning,active learningrefers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results coming from complex experiments) are costly to obtain. In such scenarios, an active learning algorithm aims at identifying data points that, if labeled and used for training, would most improve the learned model. Labels are then obtained only for the most promising data points. This speeds up learning and reduces labeling costs. Support vector machine (SVM) classifiers are particularly well‐suited for active learning due to their convenient mathematical properties. They perform linear classification, typically in a kernel‐induced feature space, which makes expressing the distance of a data point from the decision boundary straightforward. Furthermore, heuristics can efficiently help estimate how strongly learning from a data point influences the current model. This information can be used to actively select training samples. After a brief introduction to the active learning problem, we discuss different query strategies for selecting informative data points and review how these strategies give rise to different variants of active learning withSVMs.This article is categorized under:Technologies > Machine Learning

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion

We propose and evaluate in this paper a combination of Active Learning and Multiple Classifiers approaches for corpus annotation and concept indexing on highly imbalanced datasets. Experiments were conducted using TRECVID 2008 data and protocol with four different types of video shot descriptors, with two types of classifiers (Logistic Regression and Support Vector Machine with RBF kernel) and with two different active learning strategies (relevance and uncertainty sampling). Results show that the Multiple Classifiers approach significantly increases the effectiveness of the Active Learning. On the considered dataset, the best performance is achieved when 15 to 30% of the corpus is annotated for individual descriptors and when 10 to 15% of the corpus is annotated for their fusion.