Handling of Imbalanced Data in Text Classification: Category-Based Term Weights - Natural Language Processing and Text Mining - page 177

information gain (IG) and relevance frequency (RF) [30], by replacing the idf term
with the feature selectionvalue in theclassic tfidf weightingschemes. Therefore,
schemes arelargelyformulatedinaform as tf · ( feature value )(TFFV). Table 10.5
shows all 16 weightingschemes testedinour experiments and their mathematic for-
mations. Please note that basicallythe majority ofTFFV schemes arecomposed of
two items, i.e., the normaliz ed term frequency tf ( t i ,d j ) / max[ tf ( d j )] and the term's
feature value, e.g.,

√ N ( AD−BC )
√ ( A + C )( B + D )( A + B )( C + D ) in thecorrelation coe cient scheme,

where tf ( t i ,d j ) is the frequency ofterm t i in the document d j and max[ tf ( d j )]is
the maximum frequency ofaterm in the document d j . Theonlydifferent ones are
TFIDF weighting, 'ltc' form and the normalized'ltc' form asspecifiedinTable 10.5.

Table 10.5. All weightingschemes testedintheexperiments and their mathematic
formations, where the normalizedterm frequency ntf is definedas

tf ( t i ,d j )
max[ tf ( d j )]

Weighting Scheme Name MathematicF or mations

√ N ( AD−BC )

√ ( A + C )( B + D )( A + B )( C + D )

tf· CorrelationCoef. CC ntf ·

N ( AD−BC ) 2
( A + C )( B + D )( A + B )( C + D )

tf· Chi-square ChiS ntf ·

ntf · ( N log

AN
( A + B )( A + C )

+ N log

CN
( C + D )( A + C )

tf· Information Gain IG )

tf· Odds Ratio OddsR ntf · log( AD/BC )

ntf · log(1+ A + B
B

tf· Relevance Freq. RF )

N
N ( t

TFIDF 1] TFIDF ntf · log( )

i )

N
N ( t

tfidf -ltc ltc tf ( t i ,d j ) · log( )

i )

tf idf ltc

Normalized ltc nltc

ntf · log(1+ B

A
C )

Cat. BasedTerm Wt. 1 CBTW 1

log(1+ B + C )

Cat. BasedTerm Wt.2CBTW 2 ntf ·

ntf · log(1+ B ) log(1+ C ) Cat. BasedTerm Wt. 3 CBTW 3

ntf · log[(1+ B )(1+ C )]

Cat. BasedTerm Wt. 4 CBTW 4

ntf · log(1+ A + B
B

A + C
C )

Cat. BasedTerm Wt. 5 CBTW 5

ntf · log(1+ A + B
B

+ A + C
C

Cat. BasedTerm Wt. 6 CBTW 6 )

Cat. BasedTerm Wt. 7 CBTW 7 ntf · log(1+ A + B

B

) log(1+ A + C
C

)

Cat. BasedTerm Wt. 8 CBTW 8 ntf · log[(1+ A + B

B

)(1+ A + C
C

)]

Major standard text preprocessingsteps were appliedinour experiments, includ-
ingstopword and punctuationremoval, and stemming. However, feature selection
wasskipped and all termsleft after stopword and punctuationremoval were kept
as features. In our experiments we usedthe SVM implementation calledSVM Light
[19,20]. We usedthelinear functionas its kernel function,since previous work has
shown that thelinear function can deliver evenbetterperformance without tedious
parameter tuning in TC [19,21]. As forthe performance measurement, precision,
recall and the harmoniccombination ofprecision and recall, i.e., the F 1 value, were
calculated [1, 36]. Performance was assessedbased on five-fold cross validation. Since