Feature selection is an essential part in the data preprocessing. In the text classification, most of the previous feature selection algorithms rarely consider the redundancy between features. This paper focuses on eliminating redundancy. After modifying the formula of feature correlation of original fast correlation-based filter (FCBF) and updating the algorithm strategy, we propose a new approach named improved feature size customized fast correlation-based filter (IFSC-FCBF). In addition, we combine IFSC-FCBF with Naive Bayes (NB) classifier for text classification, and test it in four typical text corpus data sets. The results demonstrate that with the same feature size, IFSC-FCBF method has the advantages of higher accuracy and shorter running time than other methods.
ShangC., LiM., FengS., et al., Feature selection via maximizing global information gain for text classification[J], Knowledge-Based Systems54 (2013), 298–309.
2.
SaltonG., WongA. and YangC.S., A vector space model for automatic indexing[J], Communications of the ACM18(11) (1975), 613–620.
3.
YuL. and LiuH., Efficient feature selection via analysis of relevance and redundancy[J], Journal of Machine Learning Research5 (2004), 1205–1224.
4.
KohaviR. and JohnG.H., Wrappers for feature subset selection[J], Artificial Intelligence97(1-2) (1997), 273–324.
5.
JohnG.H., KohaviR. and PflegerK., Irrelevant features and the subset selection problem[M]//Machine Learning Proceedings 1994. Morgan Kaufmann, (1994), 121–129.
6.
HuangJ., CaiY. and XuX., A hybrid genetic algorithm for feature selection wrapper based on mutual information[J], Pattern Recognition Letters28(13) (2007), 1825–1844.
7.
AzamN. and YaoJ.T., Comparison of term frequency and document frequency based feature selection metrics in text categorization[J], Expert Systems with Applications39(5) (2012), 4760–4768.
8.
FormanG., An extensive empirical study of feature selection metrics for text classification[J], Journal of Machine Learning Research3 (2003), 1289–1305.
9.
ShangW., HuangH., ZhuH., et al., A novel feature selection algorithm for text categorization[J], Expert Systems with Applications33(1) (2007), 1–5.
10.
LeeC. and LeeG.G., Information gain and divergence-based feature selection for machine learning-based text categorization[J], Information Processing & Management42(1) (2006), 155–165.
11.
UysalA.K. and GunalS., A novel probabilistic feature selection method for text classification[J], Knowledge-Based Systems36 (2012), 226–235.
12.
YuL., LiuH., Feature selection for high-dimensional data: A fast correlation-based filter solution[C], Proceedings of the 20th International Conference on Machine Learning (ICML-03) (2003), 856–863.
13.
LiuY., ZhangJ. and MaL., A fault diagnosis approach for diesel engines based on self-adaptive WVD, improved FCBF and PECOC-RVM[J], Neurocomputing177 (2016), 600–611.
14.
ChenJ.J., SongA. and ZhangW., A novel hybrid gene selection approach based on ReliefF and FCBF[J], International Journal of Digital Content Technology and Its Applications5(10) (2011), 404–411.
15.
GharavianD., BejaniM. and SheikhanM., Audio-visual emotion recognition using FCBF feature selection method and particle swarm optimization for fuzzy ARTMAP neural networks[J], Multimedia Tools and Applications76(2) (2017), 2331–2352.
16.
BalakrishnanS. and NarayanaswamyR., Feature selection using fcbf in type ii diabetes databases[J], International Journal of the Computer, the Internet and the Management17(1) (2009), 50–58.
17.
ŞenB. and PekerM., Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms[J], Turkish Journal of Electrical Engineering & Computer Sciences21(Sup. 1) (2013), 2092–2109.
18.
ZhangL., JiangL., LiC., et al., Two feature weighting approaches for naive Bayes text classifiers[J], Knowledge-Based Systems100 (2016), 137–144.
19.
PonteJ.M. and CroftW.B., A language modeling approach to information retrieval[C], ACM SIGIR Forum, ACM51(2) (2017), 202–208.
20.
McCallumA. and NigamK., A comparison of event models for naive bayes text classification[C], AAAI-98 Workshop on Learning for Text Categorization752(1) (1998), 41–48.
21.
WangS., JiangL., LiC., A CFS-based feature weighting approach to naive bayes text classifiers[C], International Conference on Artificial Neural Networks, Springer, Cham, (2014), 555–562.
22.
SenliolB., GulgezenG., YuL., et al., Fast Correlation Based Filter (FCBF) with a different search strategy[C], 2008 23rd international symposium on computer and information sciences, IEEE (2008), 1–4.
23.
LiuH. and YuL., Toward integrating feature selection algorithms for classification and clustering[J], IEEE Transactions on Knowledge & Data Engineering2005(4), 491–502.
24.
BidiN., ElberrichiZ., Feature selection for text classification using genetic algorithms[C], 2016 8th International Conference on Modelling, Identification and Control (ICMIC), IEEE (2016), 806–810.
25.
JiangM., LiangY., FengX., et al., Text classification based on deep belief network and softmax regression[J], Neural Computing and Applications29(1) (2018), 61–70.
26.
UysalA.K., An improved global feature selection scheme for text classification[J], Expert Systems with Applications43 (2016), 82–92.
27.
ChenJ., HuangH., TianS., et al., Feature selection for text classification with Naïve Bayes[J], Expert Systems with Applications36(3) (2009), 5432–5435.
28.
ConneauA., SchwenkH., BarraultL., et al., Very deep convolutional networks for text classification[J], arXiv preprint arXiv:1606.01781, 2016.
29.
JiangQ., WangW., HanX., et al., Deep feature weighting in Naive Bayes for Chinese text classification[C], 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), IEEE (2016), 160–164.
30.
VoraS., YangH., A comprehensive study of eleven feature selection algorithms and their impact on text classification[C], 2017 Computing Conference, IEEE (2017), 440–449.
31.
YanY., LiC. and MengS., Emotion recognition based on sparse learning feature selection method for social communication[J], Signal, Image and Video Processing (2019), 1–5.
32.
ÖzsevenT., A novel feature selection method for speech emotion recognition[J], Applied Acoustics146 (2019), 320–326.
33.
LeeC. and LeeG.G., Information gain and divergence-based feature selection for machine learning-based text categorization[J], Information Processing & Management42(1) (2006), 155–165.
