Meta-learning of Text Classification Tasks

Madrid, Jerry A.

doi:10.1007/978-3-030-33904-3_10

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…So far, domain specific meta-features are not used in the anomaly detection domain. They are only used in text classification, where domain-based knowledge present vocabulary length, words overlap, number of text categories, corpus hardness, domain broadness, and similar [25], [31].…”

Section: Meta-featuresmentioning

confidence: 99%

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

et al. 2021

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A growing number of research papers shed light on automated machine learning (AutoML) frameworks, which are becoming a promising solution for building complex machine learning models without human expertise and assistance. The key challenge in enabling AutoML frameworks to build an efficient model for anomaly detection tasks is to determine the best underlying model for a given task and optimization metric. The meta-learning approaches based on a set of meta-features that describes data properties can enable efficient model selection in AutoML frameworks. The existing meta-learning approaches based on statistical and information-theoretic meta-features require large amounts of data and computational resources to extract data properties. This paper proposes a novel set of meta-features for model selection in anomaly detection tasks based on domain-specific properties of data which overcomes the shortcomings of existing meta-features by introducing simple but effective meta-features that can be efficiently extracted or estimated by using a low amount of data. Experiments with 63 datasets from different repositories with varying schemas show that the proposed set of meta-features achieves an accuracy of 87% for model selection, while the achieved accuracy for simple meta-features is 74%, for statistical meta-features 68%, for information theory meta-feature 70%, and for a comprehensive set of meta-features by pyMFE 73%. This demonstrates that the proposed set can be adopted by AutoML frameworks across a diverse range of domains.

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Section: Meta-featuresmentioning

confidence: 99%

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

et al. 2021

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“…Angelov [5] presents the top2vec method for modeling topics, which leverages joint document and word semantic embedding to find topic vectors. Madrid [6] presents a approach to text mining and classification based on meta-learning method.…”

Section: Related Workmentioning

confidence: 99%

“…Different classifiers are tested using labeled texts. Each abstract receives a label corresponding to its respective class (1)(2)(3)(4)(5)(6)(7)(8). The classifiers are trained with 70% of the stratified data, and 30% of the data are used for validation.…”

Section: Classifying the Texts Based On The Problemmentioning

confidence: 99%

Automatic evaluation of scientific abstracts through natural language processing

Lopes¹,

Vieira²,

Lira³

2021

Preprint

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This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.

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“…Although data processing, including feature selection and extraction, have been considered as components of pipelines generated by AutoML techniques, the feature engineering process by itself has received little attention from the community. It is only recently that efforts aiming to process raw data directly are emerging, see [36,51,43,44,48]. We believe this research venue will be decisive for the full automation of the AutoML process.…”

Section: Open Issues and Research Opportunitiesmentioning

confidence: 99%

Automated Machine Learning -- a brief review at the end of the early years

Escalante

2020

Preprint

Self Cite

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Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are therefore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined.1 Please note that although model selection and other efforts for hyperparameter optimization have been out there for decades, see e.g., [24]; this chapter focuses on full model or pipeline selection and design [11,33].

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Meta-learning of Text Classification Tasks

Cited by 4 publications

References 9 publications

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

Automatic evaluation of scientific abstracts through natural language processing

Automated Machine Learning -- a brief review at the end of the early years

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