Incorporating Diversity and Density in Active Learning for Relevance Feedback

Xu, Zuobing; Akella, Ram; Zhang, Yi

doi:10.1007/978-3-540-71496-5_24

Cited by 133 publications

(102 citation statements)

References 7 publications

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“…In addition to the approach developed in this paper, approaches combining active learning [4,23] with exploration strategies from RL could be developed.…”

Section: Resultsmentioning

confidence: 99%

Balancing Exploration and Exploitation in Learning to Rank Online

Hofmann

Whiteson

Rijke

2011

Lecture Notes in Computer Science

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Abstract. As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has already been learned to produce results that are acceptable to users. We formulate this challenge as an exploration-exploitation dilemma and present the first online learning to rank algorithm that works with implicit feedback and balances exploration and exploitation. We leverage existing learning to rank data sets and recently developed click models to evaluate the proposed algorithm. Our results show that finding a balance between exploration and exploitation can substantially improve online retrieval performance, bringing us one step closer to making online learning to rank work in practice.

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“…In addition to the approach developed in this paper, approaches combining active learning [4,23] with exploration strategies from RL could be developed.…”

Section: Resultsmentioning

confidence: 99%

Balancing Exploration and Exploitation in Learning to Rank Online

Hofmann

Whiteson

Rijke

2011

Lecture Notes in Computer Science

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“…Similarly, Xu et al (2007) use clustering to construct sets of queries for batch-mode active learning with SVMs. Specifically, they query the centroids of clusters of instances that lie closest to the decision boundary.…”

Section: Related Workmentioning

confidence: 99%

Lookahead selective sampling for incomplete data

Abdallah

Shimshoni

2016

International Journal of Applied Mathematics and Computer Science

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Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed two versions of selective sampling. The first one integrates a distance function that can measure the similarity between pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the ensemble clustering instance space (LSS-EC). To construct the cluster matrix, we use the k-means and mean shift clustering algorithms especially modified to deal with incomplete datasets. We tested our algorithms on six standard numerical datasets from different fields. On these datasets we simulated missing values and compared the performance of the LSS and LSS-EC algorithms for incomplete data to two other basic methods. Our experiments show that the suggested selective sampling algorithms outperform the other methods.

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“…The need to inject "diversity" into active label selection criteria has been considered in classification work [4,36,29], to help ensure all parts of the feature space are explored. We propose a novel diversity bias for active rank learning.…”

Section: Diversity In Active Learningmentioning

confidence: 99%