Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Osugi, T.T.; Kim, Deng; Scott, Stephen

doi:10.1109/icdm.2005.33

Cited by 94 publications

(90 citation statements)

References 14 publications

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“…We propose a sampling scheme that covers two aspects: exploration and exploitation. This coincides with the ideas proposed in [14] that an active learning scheme should not only refine the decision boundaries but also needs to verify the current hypothesis. The prior data distribution plays an important role.…”

Section: Selection Of Patterns To Querymentioning

confidence: 63%

Adaptive Active Classification of Cell Assay Images

Cebron

Berthold

2006

Lecture Notes in Computer Science

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Abstract. Classifying large datasets without any a-priori information poses a problem in many tasks. Especially in the field of bioinformatics, often huge unlabeled datasets have to be explored mostly manually by a biology expert. In this work we consider an application that is motivated by the development of high-throughput microscope screening cameras. These devices are able to produce hundreds of thousands of images per day. We propose a new adaptive active classification scheme which establishes ties between the two opposing concepts of unsupervised clustering of the underlying data and the supervised task of classification. Based on Fuzzy c-means clustering and Learning Vector Quantization, the scheme allows for an initial clustering of large datasets and subsequently for the adjustment of the classification based on a small number of carefully chosen examples. Motivated by the concept of active learning, the learner tries to query the most informative examples in the learning process and therefore keeps the costs for supervision at a low level. We compare our approach to Learning Vector Quantization with random selection and Support Vector Machines with Active Learning on several datasets.

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Section: Selection Of Patterns To Querymentioning

confidence: 63%

Adaptive Active Classification of Cell Assay Images

Cebron

Berthold

2006

Lecture Notes in Computer Science

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“…Therefore, in each step, not only the immediate effect of the action is important, but also its long term effect becomes important. This approach has already been used in the active learning literature for the classification problem [14].…”

Section: Proposed Active Learning Methods For Matrix Factorizationmentioning

confidence: 99%

Non-myopic active learning for recommender systems based on Matrix Factorization

Karimi

Freudenthaler

Νανόπουλος

et al. 2011

2011 IEEE International Conference on Information Reuse &Amp; Integration

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Abstract-Recommender systems help Web users to address information overload. However, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. In this paper, we consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user. The proposed method is based on Matrix Factorization (MF) which is a strong prediction model for recommender systems. First, the proposed method explores the latent space to get closer to the optimal new user parameters. Then, it exploits the learned parameters and slightly adjusts them. The results show that beside improving the accuracy of recommendation, MF approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems.

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“…Osugi et al [23] propose an active learning algorithm that balances the exploration and exploitation while selecting a new instance for labeling by the expert at each step. The algorithm randomly chooses between exploration and exploitation at each round and receives feedback on the effectiveness of the exploration step, based on the performance of the classifier trained on the explored instance.…”

Section: A Related Work On Active Learningmentioning

confidence: 99%

Active learning for aspect model in recommender systems

Karimi

Freudenthaler

Νανόπουλος

et al. 2011

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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Abstract-Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.

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Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Abstract: Abstract

Cited by 94 publications

References 14 publications

Adaptive Active Classification of Cell Assay Images

Adaptive Active Classification of Cell Assay Images

Non-myopic active learning for recommender systems based on Matrix Factorization

Active learning for aspect model in recommender systems

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