Application of Tolerance Rough Sets in Structured and Unstructured Text Categorization: A Survey

Ramanna, Sheela; Peters, James F.; Sengoz, Cenker

doi:10.1007/978-3-319-54966-8_6

Cited by 5 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mathematically, tolerance relations are reflexive and symmetric but are not necessarily transitive, so the classes induced by such relations may have overlapping members. An in-depth survey of application of tolerance rough sets in text categorization can be found in Ramanna et al 26 Rough Sets: Formal Notation Let U be a finite, non-empty universe of objects and let R4U3U denote a binary relation on the universe U. R is called an indiscernibility relation and for rough sets, it has to be an equivalence relation. The pair ðU; RÞ = A constitutes an approximation space.…”

Section: Theorymentioning

confidence: 99%

See 1 more Smart Citation

Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

Moghaddam¹,

Ramanna

2020

Patterns

Self Cite

View full text Add to dashboard Cite

Section: Theorymentioning

confidence: 99%

“…Contexts in zone 4 will not contribute at all, since they suggest no resemblance between np i andnp j . 26 The approximation operators will assess whether these linguistic patterns can be categorized as ''certain,'' ''uncertain,'' ''negative,'' or in ''boundary'' zone of a semantic type.…”

Section: Theorymentioning

confidence: 99%

Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

Moghaddam¹,

Ramanna

2020

Patterns

Self Cite

View full text Add to dashboard Cite

“…The regions are depicted as squares only for the sake of illustration, but they can be of arbitrary shape. We should note that each granule can contain an arbitrary number of objects or may be empty [51]. The difference between these two regions (also known as the boundary region) contains objects that may belong to more than one category or class or concept.…”

Section: Rough Sets and Tolerance Rough Setsmentioning

confidence: 99%

“…Tolerance relations can be considered as generalizations of equivalence relations [48]. A history of tolerance rough sets from an axiomatic point of view can be found in [51].…”

Section: Uppermentioning

confidence: 99%

Short Text Classification with Tolerance Near Sets

Patel¹

View full text Add to dashboard Cite

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.

show abstract

“…1 shows the results of applying Genetic Algorithm option in RSES. Table7.2 shows the results of FRNN algorithm using option 1 given in Chapter.…”

mentioning

confidence: 99%

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

Ashrafi¹

View full text Add to dashboard Cite

COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges in populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids’ beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of medicinal cannabis since COVID-19 was declared a pandemic. Furthermore, evidence points to a correlation between mental health and sleep patterns. The objective of this research is threefold: i) to assess the relationship of the clinical delivery of cannabinoid medicine, by utilizing machine learning, to anxiety, depression and sleep scores; ii) to discover patterns based on patient features such as specific cannabis recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (GAD7, PHQ9 and PSQI) scores over a period of time (including during the COVID timeline); and iii) to predict whether new patients could potentially experience either an increase or decrease in clinical assessment tool scores. The dataset for this thesis was derived from patient visits to Ekosi Health Centres in Manitoba, Canada and Ontario, Canada from January, 2019 to April, 2021. Extensive pre-processing and feature engineering was performed. To determine the outcome of a patients treatment, a class feature (Worse, Better, or No Change) indicative of their progress or lack thereof due to the treatment received was introduced. Three well-known supervised machine learning models (tree-based, rule-based and nearest neighbour) were trained on the patient dataset. In addition, seven rough and rough-fuzzy hybrid methods were also trained on the same dataset. All experiments were conducted using a 10-fold CV method. Sensitivity and specificity measures were higher in all classes with rough and rough-fuzzy hybrid methods. The highest accuracy of 99.15% was obtained using the rule-based rough-set learning method.

show abstract

Application of Tolerance Rough Sets in Structured and Unstructured Text Categorization: A Survey

Cited by 5 publications

References 18 publications

Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

Short Text Classification with Tolerance Near Sets

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

Contact Info

Product

Resources

About