Feature selection through visualisation for the classification of online reviews
Indiana University-Purdue University Indianapolis (IUPUI) === The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classification into fa...
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| Language: | en_US |
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2017
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| Online Access: | http://hdl.handle.net/1805/12483 https://doi.org/10.7912/C2XM1D |
| Summary: | Indiana University-Purdue University Indianapolis (IUPUI) === The purpose of this work is to prove that the visualization is at least as powerful
as the best automatic feature selection algorithms. This is achieved by applying
our visualization technique to the online review classification into fake and genuine
reviews. Our technique uses radial chart and color overlaps to explore the best
feature selection through visualization for classification. Every review is treated as a
radial translucent red or blue membrane with its dimensions determining the shape
of the membrane. This work also shows how the dimension ordering and combination
is relevant in the feature selection process. In brief, the whole idea is about giving
a structure to each text review based on certain attributes, comparing how different
or how similar the structure of the different or same categories are and highlighting
the key features that contribute to the classification the most. Colors and saturations
aid in the feature selection process. Our visualization technique helps the user get
insights into the high dimensional data by providing means to eliminate the worst
features right away, pick some best features without statistical aids, understand the
behavior of the dimensions in different combinations. |
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