Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015)
0.01
0.0072827823 = product of:
0.02913113 = sum of:
0.02913113 = weight(_text_:information in 2339) [ClassicSimilarity], result of:
0.02913113 = score(doc=2339,freq=16.0), product of:
0.08850355 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.050415643 = queryNorm
0.3291521 = fieldWeight in 2339, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=2339)
0.25 = coord(1/4)
- Abstract
- Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565
