Support Vector Machine Active Learning Algorithms with Query-by-Committee Versus Closest-to-Hyperplane Selection

Bloodgood, Michael

doi:10.1109/icsc.2018.00029

Cited by 19 publications

(15 citation statements)

References 19 publications

(40 reference statements)

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“…to the separating hyper-plane is an indicator of uncertainty, with the example having the lowest distance being most uncertain [83].…”

Section: Positive Certain and Uncertainmentioning

confidence: 99%

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Zajec¹,

Rožanec²,

Trajkova³

et al. 2021

Preprint

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This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

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“…to the separating hyper-plane is an indicator of uncertainty, with the example having the lowest distance being most uncertain [83].…”

Section: Positive Certain and Uncertainmentioning

confidence: 99%

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Zajec¹,

Rožanec²,

Trajkova³

et al. 2021

Preprint

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“…We also use the closest-to-hyperplane selection algorithm with SVM for active learning [35], [36], [16]. This is because previous work has shown that it has better performance over other selection algorithms used [37]. For each iteration of training, the number of samples used is determined by a batch percent, bp.…”

Section: A Iterative Learning Setupmentioning

confidence: 99%

Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning

Orth

Bloodgood

2020

2020 IEEE 14th International Conference on Semantic Computing (ICSC)

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When creating text classification systems, one of the major bottlenecks is the annotation of training data. Active learning has been proposed to address this bottleneck using stopping methods to minimize the cost of data annotation. An important capability for improving the utility of stopping methods is to effectively forecast the performance of the text classification models. Forecasting can be done through the use of logarithmic models regressed on some portion of the data as learning is progressing. A critical unexplored question is what portion of the data is needed for accurate forecasting. There is a tension, where it is desirable to use less data so that the forecast can be made earlier, which is more useful, versus it being desirable to use more data, so that the forecast can be more accurate. We find that when using active learning it is even more important to generate forecasts earlier so as to make them more useful and not waste annotation effort. We investigate the difference in forecasting difficulty when using accuracy and F-measure as the text classification system performance metrics and we find that F-measure is more difficult to forecast. We conduct experiments on seven text classification datasets in different semantic domains with different characteristics and with three different base machine learning algorithms. We find that forecasting is easiest for decision tree learning, moderate for Support Vector Machines, and most difficult for neural networks.

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“…We take advantage of that body of research to select our set of experimental approaches, which include sample selection via Gaussian mixture models [17], [31] and Determinantal Point Processes (DPPs) [38], which have proven effective in modeling diversity [21], [76]. Using supervised learners as the active learning techniques [5], [66] are not suitable for our current study since we concentrate on building a language model without prior knowledge [35].…”

Section: Related Workmentioning

confidence: 99%

Sampling Approach Matters: Active Learning for Robotic Language Acquisition

Pillai

Raff

Ferraro

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples.

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Support Vector Machine Active Learning Algorithms with Query-by-Committee Versus Closest-to-Hyperplane Selection

Cited by 19 publications

References 19 publications

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning

Sampling Approach Matters: Active Learning for Robotic Language Acquisition

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