Book Classification Via Fuzzy Logic Jeremy Keer

If possible, use clustering ANN for a visual output Project Goals Develop a fuzzy logic rule set to classify the content of fantasy and science fiction books based upon genre and hardness Allow a user to classify a book with cursory knowledge with the purpose of finding whether it is similar to other styles they have enjoyed If possible, use clustering ANN for a visual output

Line between genres can be blurry at times Why Use Fuzzy Logic? Line between genres can be blurry at times Existing deeper classification systems tend to rely on tags that ultimately don’t offer much information on the actual content of the story

Data is gathered via questionnaires Data Set Generation Data is gathered via questionnaires Questions must be formulated such that answers can be given reasonably in the form of a scale Two types of data sets used: Shallow set answerable based largely on summary and tags Deep set meant to be answered by someone who has read the book so that it can be accurately be classified

Classification Methods Data is analyzed and given values on two spectrums: Science Fiction vs Fantasy: while sometimes seeming to be a binary choice, tends to actually be much more complex Hardness: hardest is “real life,” becomes progressively softer as more fantastical elements are included

Reducing output regions of rule set should work for classification Expected Results Questions work well, but would like to add more to improve classification Finding questions that fit well to scales is the major problem Reducing output regions of rule set should work for classification For example, three regions on the SF/Fantasy scale and five regions for each of these for hardness