Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015)
0.03
0.026731037 = product of:
0.053462073 = sum of:
0.053462073 = product of:
0.10692415 = sum of:
0.10692415 = weight(_text_:mining in 2339) [ClassicSimilarity], result of:
0.10692415 = score(doc=2339,freq=2.0), product of:
0.28585905 = queryWeight, product of:
5.642448 = idf(docFreq=425, maxDocs=44218)
0.05066224 = queryNorm
0.37404498 = fieldWeight in 2339, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.642448 = idf(docFreq=425, maxDocs=44218)
0.046875 = fieldNorm(doc=2339)
0.5 = coord(1/2)
0.5 = coord(1/2)
- 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.
