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Saved by uncleflo on December 23rd, 2018.
I am working on text classification using SVM. In a paper (Fuzzy Support vector machine for multi-class text categorization) the author has reduced the features(words) by applying the following criteria: "Eliminate the words that are ICF>log2, Uni<0.2 and TF_IDF<26". My question is how can we find TF_IDF value of a word. TF is a local measure and IDF is a global measure. TF_IDF gives different value for a word in each document. TF-IDF is the acronym for Term Frequency–Inverse Document Frequency. This metric aims at estimating how important is a keyword not only in a particular document, but rather in a whole collection of documents (corpus). Actually, a lot of common words like articles or conjunctions may appear several times in a document but they are not relevant as key-concepts to be indexed or searched. TF (Term Frequency) provides a measure about how frequently a term occurs in a document.
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