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These are differing modalities of uncertain knowledge that are supported by many models in the literature. The new structure that embraces all these features simultaneously is called interval-valued multi-polar fuzzy soft set (IV<jats:italic>m<\/jats:italic>FSS, for short). An enhanced combination of interval-valued <jats:italic>m<\/jats:italic>-polar fuzzy (IV<jats:italic>m<\/jats:italic>F) sets and soft sets produces this model. As such, the theory of IV<jats:italic>m<\/jats:italic>FSSs constitutes both an interval-valued multipolar-fuzzy generalization of soft set theory; a multipolar generalization of interval-valued fuzzy soft set theory; and an interval-valued generalization of multi-polar fuzzy set theory. Some fundamental operations for IV<jats:italic>m<\/jats:italic>FSSs, including intersection, union, complement, \u201cOR\u201d, \u201cAND\u201d, are explored and investigated through examples. An algorithm is developed to solve decision-making problems having data in interval-valued <jats:italic>m<\/jats:italic>-polar fuzzy soft form. It is applied to two numerical examples. In addition, three parameter reduction approaches and their algorithmic formulation are proposed for IV<jats:italic>m<\/jats:italic>FSSs. They are respectively called parameter reduction based on optimal choice, rank based parameter reduction, and normal parameter reduction. Moreover, these outcomes are compared with existing interval-valued fuzzy methods; relatedly, a comparative analysis among reduction approaches is investigated. Two real case studies for the selection of best site for an airport construction and best rotavator are studied.<\/jats:p>","DOI":"10.1007\/s10462-021-10027-x","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T12:02:40Z","timestamp":1624536160000},"page":"5541-5582","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Parameter reduction analysis under interval-valued m-polar fuzzy soft information"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-7962","authenticated-orcid":false,"given":"Muhammad","family":"Akram","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5316-3063","authenticated-orcid":false,"given":"Ghous","family":"Ali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4533-9281","authenticated-orcid":false,"given":"Jos\u00e9 Carlos R.","family":"Alcantud","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"issue":"1","key":"10027_CR1","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.3233\/JIFS-192203","volume":"39","author":"A Adeel","year":"2020","unstructured":"Adeel A, Akram M, Yaqoob N, Chammam W (2020) Detection and severity of tumor cells by graded decision-making methods under fuzzy $$N$$-soft model. 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