improving music mood classification using lyrics, audio and social tags

accuracy =
TP + TN
TP + FN + FP + TN
; precision =
TP
; recall =
TP + FP
TP
TP + FN
F β =
2
( β + 1) * precision * recall
the importance of recall
, β ≥ 0, whereβ
=
.
2
β * precision + recall
the importance of precision
Usually β = 1 giving equal importance to precision and recall: F 1 =
2 * precision * recall
.
precision + recall
Accuracy has been extensively adopted in binary classification evaluations in text
categorization. In MIR, especially MIREX, accuracy has been commonly reported in evaluating
classification tasks. Therefore, accuracy will be used as the classification performance measure
in this dissertation research.
In evaluations of multiple categories, a concise and reliable measure of average performance
is desirable. There are two approaches to calculating the average performance over all categories:
micro-average and macro-average. Micro-average first gets the sums for all four cells in the
contingency table (Table 4.1) across categories before calculating the final performance measure
using the above formulas, while macro-average calculates the performance measures for each
category and then takes the mean as the final score. Micro-averaging gives equal weight to each
instance and therefore tends to be dominated by the classifier’s performance on big categories.
Macro-averaging gives equal weight to each category, regardless of its size. Thus the two
measures may give very different scores. This dissertation research puts equal emphasis on each
mood category and thus macro-averaged measures are adopted for evaluation and comparison.
In terms of splitting data into training and testing sets, both multiple randomized hold out
tests and cross validation are often used in MIR classification evaluations. In a hold-out test the
48