A novel multiple kernel fuzzy topic modeling technique for biomedical data

BMC Bioinformatics

Table 2 Classification results (muchmore springer bilingual corpus)

Method	AC (%)	Precision	Recall	F1-Score	K
LSA [5]	57.65	0.6667	0.7221	0.6933	50
LDA [4]	60.95	0.6938	0.7356	0.7141	50
FKLSA(Entropy) [6]	97.66	0.955	0.9554	0.977	50
FKLSA(IDF) [6]	95.90	0.937	0.935	0.959	50
FKLSA(Normal) [6]	91.22	0.890	0.894	0.912	50
FKLSA(ProbIDF) [6]	97.66	0.954	0.953	0.977	50
FKTM [7]	98.29	0.9880	0.9883	0.9880	50
MKFTM	99.04	0.9975	0.9978	0.9975	50
LSA [5]	56.19	0.6676	0.6791	0.6733	100
LDA [4]	58.85	0.6854	0.7011	0.6932	100
FKLSA(Entropy) [6]	96.49	0.943	0.942	0.965	100
FKLSA(IDF) [6]	98.24	0.961	0.960	0.982	100
FKLSA(Normal) [6]	92.39	0.902	0.900	0.924	100
FKLSA(ProbIDF) [6]	97.66	0.955	0.952	0.977	100
FKTM [7]	98.87	0.9879	0.9841	0.9844	100
MKFTM	99.62	0.9974	0.9936	0.9939	100
LSA [5]	62.67	0.7091	0.7536	0.7285	150
LDA [4]	59.23	0.6991	0.6791	0.6890	150
FKLSA(Entropy) [6]	95.90	0.937	0.935	0.959	150
FKLSA(IDF) [6]	97.66	0.955	0.952	0.977	150
FKLSA(Normal) [6]	95.32	0.932	0.931	0.953	150
FKLSA(ProbIDF) [6]	97.07	0.950	0.952	0.971	150
FKTM [7]	98.97	0.9822	0.9882	0.9886	150
MKFTM	99.69	0.9917	0.9976	0.9980	150
LSA [5]	60.00	0.6980	0.7020	0.9886	200
LDA [4]	63.42	0.7039	0.7765	0.7000	200
FKLSA(Entropy) [6]	97.07	0.950	0.9501	0.7384	200
FKLSA(IDF) [6]	97.66	0.955	0.9553	0.971	200
FKLSA(Normal) [6]	92.39	0.901	0.902	0.977	200
FKLSA(ProbIDF) [6]	97.66	0.955	0.950	0.924	200
FKTM [7]	98.86	0.9883	0.9870	0.977	200
MKFTM	99.61	0.9978	0.9966	0.965	200

ISSN: 1471-2105