Academic literature on the topic 'Minority class boosted framework'
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Journal articles on the topic "Minority class boosted framework"
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Zhang, Jue, Li Chen, and Fazeel Abid. "Prediction of Breast Cancer from Imbalance Respect Using Cluster-Based Undersampling Method." Journal of Healthcare Engineering 2019 (October 16, 2019): 1–10. http://dx.doi.org/10.1155/2019/7294582.
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To overcome the two-class imbalanced problem existing in the diagnosis of breast cancer, a hybrid of K-means and Boosted C5.0 (K-Boosted C5.0) is proposed which is based on undersampling. K-means is utilized to select the informative samples near the boundary. During the training phase, the K-means algorithm clusters the majority and minority instances and selects a similar number of instances from each cluster. Boosted C5.0 is then used as the classifier. As there is one different instance selection factor via clustering that encourages the diversity of the training subspace in K-Boosted C5.0, it would be a great advantage to get better performance. To test the performance of the new hybrid classifier, it is implemented on 12 small-scale and 2 large-scale datasets, which are the often used datasets in class imbalanced learning. The extensive experimental results show that our proposed hybrid method outperforms most of the competitive algorithms in terms of Matthews’ correlation coefficient (MCC) and accuracy indices. It can be a good alternative to the well-known machine learning methods.
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Lee, Sunbok, and Jae Young Chung. "The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction." Applied Sciences 9, no. 15 (July 31, 2019): 3093. http://dx.doi.org/10.3390/app9153093.
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A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis, boosted decision tree showed the optimal performance.
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Stanton-Salazar, Ricardo. "A Social Capital Framework for Understanding the Socialization of Racial Minority Children and Youths." Harvard Educational Review 67, no. 1 (January 1, 1997): 1–41. http://dx.doi.org/10.17763/haer.67.1.140676g74018u73k.
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In this article, Ricardo Stanton-Salzar offers a network-analytic framework for understanding the socialization and schooling experiences of working-class racial minority youth. Unlike many previous writers who have examined the role of "significant others," he examines the role that relationships between youth and institutional agents, such as teachers and counselors, play in the greater multicultural context in which working-class minority youth must negotiate. Stanton-Salazar provides the conceptual foundations of a framework built around the concepts of social capital and institutional support. He concentrates on illuminating those institutional and ideological forces that he believes make access to social capital and institutional support within schools and other institutional settings so problematic for working-class minority children and adolescents. Stanton-Salazar also provides some clues as to how some working-class minority youth are able to manage their difficult participation in multiple worlds, how they develop cultural strategies for overcoming various obstacles, and how they manage to develop sustaining and supportive relationships with institutional agents.
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Lin, Hsien-I., and Mihn Cong Nguyen. "Boosting Minority Class Prediction on Imbalanced Point Cloud Data." Applied Sciences 10, no. 3 (February 2, 2020): 973. http://dx.doi.org/10.3390/app10030973.
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Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).
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Krishnan, Ulagapriya, and Pushpa Sangar. "A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data." Journal of Data and Information Science 6, no. 1 (January 27, 2021): 178–92. http://dx.doi.org/10.2478/jdis-2021-0011.
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Abstract Purpose This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning. Design/methodology/approach The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling (ROS), Random Under Sampling (RUS), Synthetic Minority Oversampling TEchnique (SMOTE), ADAptive SYNthetic Sampling (ADASYN), Edited Nearest Neighbor (ENN), and Condensed Nearest Neighbor (CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified. Findings This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data. Research limitations The testing was carried out with limited dataset and needs to be tested with a larger dataset. Practical implications This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance. Originality/value This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.
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Asam, Muhammad, Shaik Javeed Hussain, Mohammed Mohatram, Saddam Hussain Khan, Tauseef Jamal, Amad Zafar, Asifullah Khan, Muhammad Umair Ali, and Umme Zahoora. "Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning." Applied Sciences 11, no. 21 (November 8, 2021): 10464. http://dx.doi.org/10.3390/app112110464.
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Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features are generated from the customized CNN architectures and are fed to a support vector machine (SVM) algorithm for malware classification, while, in the DBFS-MC framework, the discrimination power is enhanced by first combining deep feature spaces of two customized CNN architectures to achieve boosted feature spaces. Further, the detection of exceptional malware is performed by providing the deep boosted feature space to SVM. The performance of the proposed malware classification frameworks is evaluated on the MalImg malware dataset using the hold-out cross-validation technique. Malware variants like Autorun.K, Swizzor.gen!I, Wintrim.BX and Yuner.A is hard to be correctly classified due to their minor inter-class differences in their features. The proposed DBFS-MC improved performance for these difficult to discriminate malware classes using the idea of feature boosting generated through customized CNNs. The proposed classification framework DBFS-MC showed good results in term of accuracy: 98.61%, F-score: 0.96, precision: 0.96, and recall: 0.96 on stringent test data, using 40% unseen data.
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Kakkar, Misha, Sarika Jain, Abhay Bansal, and P. S. Grover. "Nonlinear Geometric Framework for Software Defect Prediction." International Journal of Decision Support System Technology 12, no. 3 (July 2020): 85–100. http://dx.doi.org/10.4018/ijdsst.2020070105.
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Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.
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Lin, Christopher, Mausam Mausam, and Daniel Weld. "Active Learning with Unbalanced Classes and Example-Generation Queries." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 6 (June 15, 2018): 98–107. http://dx.doi.org/10.1609/hcomp.v6i1.13334.
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Machine learning in real-world high-skew domains is difficult, because traditional strategies for crowdsourcing labeled training examples are ineffective at locating the scarce minority-class examples. For example, both random sampling and traditional active learning (which reduces to random sampling when just starting) will most likely recover very few minority-class examples. To bootstrap the machine learning process, researchers have proposed tasking the crowd with finding or generating minority-class examples, but such strategies have their weaknesses as well. They are unnecessarily expensive in well-balanced domains, and they often yield samples from a biased distribution that is unrepresentative of the one being learned.This paper extends the traditional active learning framework by investigating the problem of intelligently switching between various crowdsourcing strategies for obtaining labeled training examples in order to optimally train a classifier. We start by analyzing several such strategies (e.g., annotate an example, generate a minority-class example, etc.), and then develop a novel, skew-robust algorithm, called MB-CB, for the control problem. Experiments show that our method outperforms state-of-the-art GL-Hybrid by up to 14.3 points in F1 AUC, across various domains and class-frequency settings.
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Wu, Kaiyuan, Zhiming Zheng, and Shaoting Tang. "BVDT: A Boosted Vector Decision Tree Algorithm for Multi-Class Classification Problems." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (February 27, 2017): 1750016. http://dx.doi.org/10.1142/s0218001417500161.
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In this paper, we propose a powerful weak learner (Vector Decision Tree (VDT)) and a new Boosted Vector Decision Tree (BVDT) algorithm framework for the task of multi-class classification. Unlike the traditional scalar valued boosting algorithms, the BVDT algorithm directly maps the feature space to the decision space in the multi-class setting, which facilitates convenient implementations of the multi-class classification algorithms using diverse loss functions. By viewing the explicit hard threshold on the leaf node value applied in the LogitBoost as a constraint optimization problem, we further develop two new variants of the BVDT algorithm: the [Formula: see text]-BVDT and the [Formula: see text]-BVDT. The performance of the proposed algorithm is evaluated on different datasets and compared with three state-of-the-art boosting algorithms, [Formula: see text]-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The results show that the performance of the proposed algorithm ranks first in all but one dataset and reduces the test error rate by 4% up to 58% with respect to the state-of-the-art boosting algorithms based on the scalar-valued weak learner. Furthermore, we present a case study on the Abalone dataset by designing a new loss function that combines the negative log-likelihood loss function of classification problem and square loss function of regression problem.
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Wang, Ke, Qingwen Xue, and Jian John Lu. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework." International Journal of Environmental Research and Public Health 18, no. 14 (July 15, 2021): 7534. http://dx.doi.org/10.3390/ijerph18147534.
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Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive learning, this paper proposes a novel automated machine learning framework that simultaneously and automatically searches for the optimal sampling, cost-sensitive loss function, and probability calibration to handle class-imbalance problem in recognition of risky drivers. The hyperparameters that control sampling ratio and class weight, along with other hyperparameters, are optimized by Bayesian optimization. To demonstrate the performance of the proposed automated learning framework, we establish a risky driver recognition model as a case study, using video-extracted vehicle trajectory data of 2427 private cars on a German highway. Based on rear-end collision risk evaluation, only 4.29% of all drivers are labeled as risky drivers. The inputs of the recognition model are the discrete Fourier transform coefficients of target vehicle’s longitudinal speed, lateral speed, and the gap between the target vehicle and its preceding vehicle. Among 12 sampling methods, 2 cost-sensitive loss functions, and 2 probability calibration methods, the result of automated machine learning is consistent with manual searching but much more computation-efficient. We find that the combination of Support Vector Machine-based Synthetic Minority Oversampling TEchnique (SVMSMOTE) sampling, cost-sensitive cross-entropy loss function, and isotonic regression can significantly improve the recognition ability and reduce the error of predicted probability.
Dissertations / Theses on the topic "Minority class boosted framework"
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Verschae, Tannenbaum Rodrigo. "Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case." Tesis, Universidad de Chile, 2010. http://www.repositorio.uchile.cl/handle/2250/102398.
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You, Mingshan. "An Adaptive Machine Learning Framework for Access Control Decision Making." Thesis, 2022. https://vuir.vu.edu.au/43688/.
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With the increasing popularity of information systems and digital devices, data
leakage has become a serious threat on a global scale. Access control is recognised
as the first defence to guarantee that only authorised users can access sensitive
data and thus prevent data leakage. However, currently widely used attributebased
access control (ABAC) is costly to configure and manage for large-scale
information systems. Furthermore, misconfiguration and policy explosion are
two significant challenges for ABAC strategies.
In recent years, machine learning technologies have been more applied in access
control decision-making to improve the automation and performance of access
control decisions. Nevertheless, existing studies usually fail to consider the
dynamic class imbalance problem in access control and thus achieve poor performance
on minority classes. In addition, the concept drift problem caused by the
evolving user and resource attributes, user behaviours, and access environments
is also challenging to tackle.
This thesis focuses on leveraging machine learning algorithms to make more
accurate and adaptive access control decisions. Specifically, a minority class
boosted framework is proposed to address the possible concept drifts caused by
evolving users’ behaviours and system environments. Its basic idea is to adopt an
incremental batch learning strategy to update the classifier continuously. Within
this framework, a boosting window (BW) algorithm is specially designed to boost
the performance of the minority class since the minority class is fatal for data
protection in access control problems. Furthermore, to improve the overall performance
of access control, this study adopts a knowledge graph to mine the
interlinked relationships between users and resources. A knowledge graph construction algorithm is designed to build a domain-specific knowledge graph. The
constructed knowledge graph is also adopted into an online learning framework
for access control decision-making.
The proposed frameworks and algorithms are evaluated and verified through
two open-source real-world Amazon datasets. Experimental results show that the
proposed BW algorithm effectively boosts the performance of the minority class.
Furthermore, using topological features extracted from our constructed access
control knowledge graph can improve access control performance in both offline
and online learning scenarios.
Books on the topic "Minority class boosted framework"
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O'Dwyer, Conor. Coming Out of Communism. NYU Press, 2018. http://dx.doi.org/10.18574/nyu/9781479876631.001.0001.
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This book offers a close study of the rapidly evolving politics of LGBT rights in postcommunist Europe, where social attitudes have historically marginalized the issue and where the legacy of weak civil society has handicapped activism in general. What happens in societies such as these when increased exposure to transnational institutions such as the European Union and the minority-rights norms that they promote brings new visibility to LGBT issues? Is activism boosted by the infusion of resources from transnational networks? Or does transnational pressure bring backlash, inflaming antigay attitudes and driving activism underground? This study uncovers and explains the surprising divergence in the organization of LGBT activism in postcommunist Europe, focusing on Poland and the Czech Republic from the late 1980s through 2012. Hungary, Slovakia, and Romania form additional case studies. It argues that domestic backlash against transnational rights norms has been a primary catalyst for organizational development in the region’s most robust LGBT movements. It offers a comparative framework of broader relevance describing the conditions under which transnational pressure and domestic politics may interact to build robust activism, or not. This theorization offers resolution for a striking puzzle of LGBT politics in the countries examined: Why is the most organized and influential activism often found in societies where attitudes toward homosexuality are least tolerant? The book uses a multimethod research design drawing on field interviews, original sources, and participant observation to process trace how the framing of homosexuality and the organization of LGBT activism change in historical time.
Book chapters on the topic "Minority class boosted framework"
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You, Mingshan, Jiao Yin, Hua Wang, Jinli Cao, and Yuan Miao. "A Minority Class Boosted Framework for Adaptive Access Control Decision-Making." In Web Information Systems Engineering – WISE 2021, 143–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90888-1_12.
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Bassel, Leah, and Akwugo Emejulu. "Whose crisis counts?" In Minority Women and Austerity. Policy Press, 2017. http://dx.doi.org/10.1332/policypress/9781447327134.003.0003.
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In this chapter we examine in detail minority women’s institutionalised precarity in pre and post crisis France, England and Scotland. Even though minority women experience systemic social and economic inequalities, too often their experiences are erased or devalued by social movement allies and policymakers alike. This is political racelessness enacted through both political discourse and empirical data gathering and analysis. We argue that minority women experience a paradox of misrecognition—they are simultaneously invisible and hypervisible in the constructions of poverty, the crisis and austertiy. Using an intersectional framework, we will demonstrate how minority women, a heterogeneous group, experience systematic discrimination and multidimensional inequalities based on their race, class, gender and legal status. In this chapter we focus specifically on minority women’s experiences in the labour market as access to the labour market and the quality of available work is a key determinant of poverty and inequality. We also explore the particular ways in which minority women are either rendered invisible or hypervisible in key social policies meant to address their routinised inequalities.
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Zhang, Huaifeng, Yanchang Zhao, Longbing Cao, Chengqi Zhang, and Hans Bohlscheid. "Rare Class Association Rule Mining with Multiple Imbalanced Attributes." In Rare Association Rule Mining and Knowledge Discovery, 66–75. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-754-6.ch005.
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In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.
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Rattansi, Ali. "6. Intersectionality and ‘implicit’ or ‘unconscious’ bias." In Racism: A Very Short Introduction, 128–46. Oxford University Press, 2020. http://dx.doi.org/10.1093/actrade/9780198834793.003.0006.
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Race, class, and gender were often referred to as the ‘holy trinity’ of sociological analysis in the 1980s, because it was becoming clear that racial inequalities needed to be considered in relation to both class and gender. This mantra has led to a whole new field of research—‘intersectionality’ studies—which now includes within its research framework an understanding that age, disability, and citizenship also have differential impacts on majority and minority communities and individuals. ‘Intersectionality and “implicit” or “unconscious” bias’ provides examples of everyday racism and racial harassment and explains how research evidence shows that diversity training is highly ineffective as a tool for combating racial prejudice.
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Beider, Harris, and Kusminder Chahal. "The challenges of cross‑racial coalition building." In The Other America, 95–112. Policy Press, 2020. http://dx.doi.org/10.1332/policypress/9781447337058.003.0006.
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This chapter examines the possibilities of building cross-racial coalitions between the white working class and communities of color as the United States transitions from majority white to a minority white country. Fifty years after the campaign for civil rights and the passage of landmark legislation during the 1960s, there is little evidence of formal and sustainable cross-racial coalition building at the grassroots or grasstops level between the white working class and communities of color. White working-class communities wanted to engage with communities of color but did not have the means of engaging across racial boundaries beyond a superficial everyday level. Discussions between different communities were “soft-wired” and based on fleeting exchanges in informal spaces rather than becoming “hard-wired” in a strategic plan that can create a framework for coalition building. Stakeholders were largely ambivalent and occasionally hostile toward engaging with white working-class communities to build effective cross-racial alliances. Similar to white working-class communities in relation to communities of color, stakeholders found it challenging to engage with these groups.
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Imoagene, Onoso. "On the Horns of Racialization." In Beyond Expectations. University of California Press, 2017. http://dx.doi.org/10.1525/california/9780520292314.003.0006.
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Chapter 5 discusses the endpoints of the ethnic identification journey of the Nigerian second generation. A key endpoint is that they are integrating into the black middle class. The chapter utilizes the minority culture of mobility framework to examine how respondents’ middle class status affects how they interact with their proximal hosts and how their experiences in and interactions with white people in professional settings affect their identity. The chapter uses respondents’ experiences of racial discrimination, exhibitions of racial solidarity, voting patterns, use of class as a sorting mechanism to order interactions with proximal hosts and develop middle-class identities, and in the United States, their views on whether black immigrants and their children should benefit from affirmative action policies, to illustrate how the Nigerian second generation balance race and ethnicity and how race intersects with ethnicity and class in British and American societies. The chapter discusses how in the extremely important arena of the workplace, the experiences of British respondents differ from those of their American counterparts. They have experienced more racism and discrimination living in Britain, racism that is more often covert than overt. This chapter tells their stories of growing up different from Caribbeans and their experiences of discrimination, which has engendered feelings of not belonging to Britain.
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Léime, Áine Ní, and Wendy Loretto. "Gender perspectives on extended working life policies." In Gender, Ageing and Extended Working Life. Policy Press, 2017. http://dx.doi.org/10.1332/policypress/9781447325116.003.0003.
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This chapter documents international policy developments and provides a gender critique of retirement, employment and pension policies in Australia, Ireland, Germany, Portugal, Sweden, the UK, and the US. It assesses the degree to which the individual country's extended working life policies have adopted the agenda (increasing pension age and introducing flexible working) set out by the OECD and the EU. Policies include raising state pension age, changes in the duration of pension contribution requirements, the move from defined benefits to defined contribution pensions, policies on caring for vulnerable members of the population, policies enabling flexible working and anti-age discrimination measures. An expanded framework is used to assess the degree to which gender and other intersecting issues such as health, caring, class, type of occupation and/or membership of minority communities have (or have not) been taken into account in designing and implementing policies extending working life.
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Perrier, Maud. "Counter-Thinking from the Nursery: Theorizing Contemporary Childcare Movements." In Childcare Struggles, Maternal Workers & Social Reproduction, 21–41. Policy Press, 2022. http://dx.doi.org/10.1332/policypress/9781529214925.003.0002.
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This chapter begins with a definition of the maternal that emphasizes economic provision, community mothering and politicization regarding structural racism, sexism and racism. It details how a social reproduction lens makes visible the devaluation of the unwaged maternal labour of lower-class and racialized minority mothers, and produces them as deficient and in need of disciplining. The chapter then examines why the concept of social reproduction offers the best lens to understand 21st-century divisions between women. The chapter also discusses the studies of women's community organizing to show that the depletion of women's community activism and third sector over the last 40 years needs to be centred as the context for conceptualizing childcare movements. Contemporary and earlier literature on childcare struggles lacks conceptual frames to make sense of waged and unwaged childcare workers as a stratified sector. The chapter identifies the concepts of stratified reproduction, depletion and intimate unions as central to building this framework, and reveals how they need to be brought together into a unified theory of the social relations of maternal labour under neoliberalism.
Conference papers on the topic "Minority class boosted framework"
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Pi, Te, Xi Li, and Zhongfei (Mark) Zhang. "Boosted Zero-Shot Learning with Semantic Correlation Regularization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/362.
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We study zero-shot learning (ZSL) as a transfer learning problem, and focus on the two key aspects of ZSL, model effectiveness and model adaptation. For effective modeling, we adopt the boosting strategy to learn a zero-shot classifier from weak models to a strong model. For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations. With SCR embedded in the boosting objective, and with a self-controlled sample selection for learning robustness, we propose a unified framework, Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR). By balancing the SCR-regularized boosted model selection and the self-controlled sample selection, BZ-SCR is capable of capturing both discriminative and adaptable feature-to-class semantic alignments, while ensuring the reliability and adaptability of the learned samples. The experiments on two ZSL datasets show the superiority of BZ-SCR over the state-of-the-arts.