Application of Tolerance Near Sets to Audio Signal Classification

Singh, Ashmeet; Ramanna, Sheela

doi:10.1007/978-3-319-67588-6_13

Cited by 1 publication

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“…Resemblance is determined by considering set descriptions defined by feature vectors [42]. Descriptively near sets have been successfully applied in the following areas: content based image retrieval [15,67], solar flare detection in images [47], audio signal and music genre classification [52,63,72] and community detection in social networks with the TCD algorithm [23,19]. The TCD algorithm is now included in CDlib 4 which is Python software package that allows to extract, compare and evaluate com-munities from complex networks.…”

Section: Problem Definitionmentioning

confidence: 99%

Short Text Classification with Tolerance Near Sets

Patel¹

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Text classification is a classical machine learning application in Natural Language Processing, which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. Applications of text classification include sentiment classification and news categorization. Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. In this thesis, we implemented a modified form of a tolerance-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. The TSC algorithm is a supervised algorithm that was designed to perform short text classification with tolerance near sets (TNS). The proposed TSC algorithm uses pre-trained SBERT algorithm vectors for creating tolerance classes. The effectiveness of the TSC algorithm has been demonstrated by testing it on ten well-researched data sets. One of the datasets (Covid-Sentiment) was hand-crafted with tweets from Twitter of opinions related to COVID. Experiments demonstrate that TSC outperforms five classical ML algorithms with one dataset, and is comparable with all other datasets using a weighted F1-score measure.

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Section: Problem Definitionmentioning

confidence: 99%

Short Text Classification with Tolerance Near Sets

Patel¹

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show abstract

Application of Tolerance Near Sets to Audio Signal Classification

Cited by 1 publication

References 38 publications

Short Text Classification with Tolerance Near Sets

Short Text Classification with Tolerance Near Sets

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