Detection of a new class in a huge corpus of text documents through semi-supervised learning

Guru, D. S.; Suhil, Mahamad; Gowda, Harsha; Raju, Lavanya Narayana

doi:10.1109/icacci.2016.7732094

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…In LACU (Da, Yu, and Zhou 2014), the augment risk is introduced to adjust the separator closer to the labeled region. While in (Guru et al 2016;Masud et al 2010), clustering technique is used to construct the boundary for filtering examples of novel class.…”

Section: Open-set Classficationmentioning

confidence: 99%

“…However, in many real applications, the label set expands as more novel classes occur during the test phase. For example, in face recognition problem, the model is trained with data collected for a prefixed set of people, and then is applied to real environment with many new persons (Zhang and Patel 2017); in automated genre identification of web pages, web page genres evolve, and the predefined genre palette may not cover all the genres existing in a large corpus during the test phase (Guru et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…There are some studies trying to solve this problem in different ways. For example, (Yu et al 2017;Júnior et al 2016) detects novel data by the distance difference between known data and novel class data, (Masud et al 2010;Guru et al 2016) uses clustering to filter novel class data. Most of these methods require a large set of annotated examples from known classes to train the classification model, which however, is usually unavailable in real cases.…”

Section: Introductionmentioning

confidence: 99%

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Active Sampling for Open-Set Classification without Initial Annotation

Huang¹

2019

AAAI

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Open-set classification is a common problem in many real world tasks, where data is collected for known classes, and some novel classes occur at the test stage. In this paper, we focus on a more challenging case where the data examples collected for known classes are all unlabeled. Due to the high cost of label annotation, it is rather important to train a model with least labeled data for both accurate classification on known classes and effective detection of novel classes. Firstly, we propose an active learning method by incorporating structured sparsity with diversity to select representative examples for annotation. Then a latent low-rank representation is employed to simultaneously perform classification and novel class detection. Also, the method along with a fast optimization solution is extended to a multi-stage scenario, where classes occur and disappear in batches at each stage. Experimental results on multiple datasets validate the superiority of the proposed method with regard to different performance measures.

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Section: Open-set Classficationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Sampling for Open-Set Classification without Initial Annotation

Huang¹

2019

AAAI

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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Detection of a new class in a huge corpus of text documents through semi-supervised learning

Cited by 2 publications

References 11 publications

Active Sampling for Open-Set Classification without Initial Annotation

Active Sampling for Open-Set Classification without Initial Annotation

A review of semi-supervised learning for text classification

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