Cost-Sensitive Topical Data Acquisition From the Web

Naghibi, Mahdi; Anvari, Reza; Forghani, Ali; Minaei, Behrouz

doi:10.5121/ijdkp.2019.9304

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…There are various types of costs during a machine learning process [7]. Since computational cost is a real challenge for deep neural networks, researches proposed different methods and approaches to solve it.…”

Section: Related Workmentioning

confidence: 99%

Test-cost-sensitive Convolutional Neural Networks with Expert Branches

Naghibi¹,

Anvari²,

Forghani³

et al. 2019

SIPIJ

Self Cite

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It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost. Also, input instances have not the same difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and expert branches in some middle layers of the network. The expert branches decide to use a shallower part of the network or going deeper to the end, based on the difficulty of input instance. The expert branches learn to determine: is the current network prediction is wrong and if the given instance passed to deeper layers of the network it will generate right output; If not, then the expert branches stop the computation process. The experimental results on standard dataset CIFAR-10 show that the proposed method can train models with lower test-cost and competitive accuracy in comparison with basic models.

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Section: Related Workmentioning

confidence: 99%

Test-cost-sensitive Convolutional Neural Networks with Expert Branches

Naghibi¹,

Anvari²,

Forghani³

et al. 2019

SIPIJ

Self Cite

View full text Add to dashboard Cite

show abstract

Initial Evaluation of System for Visualizing Novices’ Unaware Search Intentions by Analyzing Behaviors of Article Selection

Onuma

Nakayama

Kawawa

et al. 2020

2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)

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Online learning agents for cost-sensitive topical data acquisition from the web

Naghibi

Anvari

Forghani

et al. 2022

IDA

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Access to one of the richest data sources in the world, the web, is not possible without cost. Often, this cost is not taken into account in data acquisition processes. In this paper, we introduce the Learning Agents (LA) method for automatic topical data acquisition from the web with minimum bandwidth usage and the lowest cost. The proposed LA method uses online learning topical crawlers. The online learning capability makes the LA able to dynamically adapt to the properties of web pages during the crawling process of the target topic, and learn an effective combination of a set of link scoring criteria for that topic. That way, the LA resolves the challenge in the mechanism of combining the outputs of different criteria for computing the value of following a link, in the formerly approaches, and increases the efficiency of the crawlers. A version of the LA method is implemented that uses a collection of topical content analyzers for scoring the links. The learning ability in the implemented LA resolves the challenge of the unclear appropriate size of link contexts for pages of different topics. Using standard metrics in empirical evaluation indicates that when non-learning methods show inefficiency, the learning capability of LA significantly increases the efficiency of topical crawling, and achieves the state of the art results.

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Cost-Sensitive Topical Data Acquisition From the Web

Cited by 3 publications

References 25 publications

Test-cost-sensitive Convolutional Neural Networks with Expert Branches

Test-cost-sensitive Convolutional Neural Networks with Expert Branches

Initial Evaluation of System for Visualizing Novices’ Unaware Search Intentions by Analyzing Behaviors of Article Selection

Online learning agents for cost-sensitive topical data acquisition from the web

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