Debiaser for Multiple Variables to enhance fairness in classification tasks     

《Information processing & management》2023,60(2):103226

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. This is also highlighted by some of the sustainable development goals proposed by the United Nations. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables (DEMV), an approach able to mitigate unbalanced groups bias (i.e., bias caused by an unequal distribution of instances in the population) in both binary and multi-class classification problems with multiple sensitive variables. The proposed method is compared, under several conditions, with a set of well-established baselines using different categories of classifiers. At first we conduct a specific study to understand which is the best generation strategies and their impact on DEMV’s ability to improve fairness. Then, we evaluate our method on a heterogeneous set of datasets and we show how it overcomes the established algorithms of the literature in the multi-class classification setting and in the binary classification setting when more than two sensitive variables are involved. Finally, based on the conducted experiments, we discuss strengths and weaknesses of our method and of the other baselines.  相似文献   

专题情报数据管理与智能分析平台的构建     

于倩倩  钱力  程冰  常志军  王慧丽  靳茜 《图书情报工作》2020,64(24):92-105

[目的/意义] 面对多学科领域、多类型用户的专题情报服务需求，建立专题情报数据管理与智能分析平台。实现专题情报分析的流程化和智能化，同时对融入专家智慧的专题情报分析过程数据进行管理，丰富服务模式，提升服务需求响应速度。[方法/过程] 在调研已有相关研究与实践分析基础上，提出平台设计思路、建设框架，对平台主要功能和关键技术进行剖析。[结果/结论] 专题情报数据管理与智能分析平台已建设完成。平台集成了多来源多类型数据，打通了从数据到分析的服务链条。嵌入了多种情报分析方法和深度学习算法，实现了多维多层次分析服务。能够对分析过程和情报分析人员历史积累数据进行管理，实现数据共享和重复利用。  相似文献   

Writer identification approach based on bag of words with OBI features     

Amal Durou  Ibrahim Aref  Somaya Al-Maadeed  Ahmed Bouridane  Elhadj Benkhelifa 《Information processing & management》2019,56(2):354-366

Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision.Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques.  相似文献   

An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification     

《Information processing & management》2023,60(2):103233

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.  相似文献   

Reliably Assessing Growth with Longitudinal Diagnostic Classification Models     

Matthew J. Madison 《Educational Measurement》2019,38(2):68-78

Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes: (1) to define and evaluate two reliability measures to be used in the application of longitudinal DCMs; (2) through simulation, demonstrate that longitudinal DCM growth estimates have increased reliability compared to longitudinal item response theory models; and (3) through an empirical analysis, illustrate the practical and interpretive benefits of longitudinal DCMs. A discussion describes how longitudinal DCMs can be used as practical and reliable psychometric models when categorical and criterion‐referenced interpretations of growth are desired.  相似文献   

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com     

Wenchao Ma  Jimmy de la Torre 《Educational Measurement》2019,38(2):114-115

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.  相似文献   

Ancient Mural Classification Method Based on Improved AlexNet Network     

Jianfang Cao  Hongyan Cui  Qi Zhang  Zibang Zhang 《文物保护研究》2020,65(7):411-423

ABSTRACT

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.  相似文献   

图书馆为区域服务的定位分析与选择     

李东来  李晓辉  何艳 《图书情报工作》2020,64(1):43-49

[目的/意义]梳理图书馆与区域的关系,不以简单的服务与被服务进行限定,而从两者包含与互动来认识图书馆为区域服务。[方法/过程]图书馆为区域服务应该从环境与定位入手,重新分析认识图书馆社会职责、重点任务、服务工作内容。[结果/结论]图书馆是区域的组成部分,应以主体意识来发展自己,完善区域构成。图书馆应遵循定位,选准重点建设,筑牢专业的服务基础,为区域的政府决策、科研开发、产业经济、民众生活等提供信息服务。  相似文献   

中外情报学论文创新性特征研究   总被引：1，自引：0，他引：1  

曹树金  闫欣阳  张倩  卓伊玲 《图书情报工作》2020,64(1):80-92

[目的/意义] 综合运用定性与定量相结合的方法对近年中外情报学论文的创新性进行分析和对比，揭示情报学领域研究的创新性特征，发现领域学术论文中创新句内部的知识关系，进行更细粒度的论文创新性分析，为研究领域创新点深层次利用提供条件，同时丰富科技论文创新性监测的途径，促进科学研究创新。[方法/过程] 从句子级创新性识别出发，选取中英文各两种情报学期刊作为样本，采用信息抽取和机器学习的方法，将创新句的抽取从现有的摘要扩展到全文，充分利用句子结构和句法特征识别领域创新内容，探讨近年中外情报学论文在创新对象、主题、类别等方面的特征，并做对比分析，最后通过对自动分类的论文集合进行定性的内容分析，总结归纳出中外情报学论文创新的表达范式。[结果/结论] 从创新的表达来看，中外情报学论文创新句的分布情况基本一致，英文期刊论文创新的表达更丰富。从创新性特征来看，英文情报学期刊论文创新主题较集中，而中文主题多样和分散；具体方法的创新是近年情报学领域的创新热点，而在研究方法上创新不足；中英文情报学期刊论文的创新性特点都反映了应用研究、实证研究的成果较多，而理论创新推动缓慢的趋势。  相似文献   

基于深度学习的中文专利自动分类方法研究   总被引：2，自引：0，他引：2  

吕璐成  韩涛  周健  赵亚娟 《图书情报工作》2020,64(10):75-85

[目的/意义] 面向当前国内专利审查和专利情报分析工作中对于海量专利分类的客观需求，设计了7种基于深度学习的专利自动分类方法，对比各种方法的分类效果，从而助力专利分类效率和效果的提升。[方法/过程] 针对传统机器学习方法存在的缺陷，基于Word2Vec、CNN、RNN、Attention机制等深度学习技术，考虑专利文本语序特征、上下文特征以及分类关键特征，设计Word2Vec+TextCNN、Word2Vec+GRU、Word2Vec+BiGRU、Word2Vec+BiGRU+TextCNN等7种深度学习模型，以中国专利为例，选取IPC主分类号的"部"作为分类依据，对比这7种模型与3种传统分类模型在中文专利分类任务中的效果。[结果/结论] 实证研究效果显示，采用考虑语序特征、上下文特征及强化关键特征的深度学习方法进行中文专利分类具有更优的分类效果。  相似文献