Research on Boold Species Ide.pngication Algorithm Based on RF_AdaBoost Model †

doi:10.7503/cjcu20190400

Abstract

Abstract: Aim ing at the requirements of non-destructive and high-efficiency analysis methods for ide.pngying human and non-human blood species, a method of blood species ide.pngication based on Random Forest combined Adaptive Boosting Algorithm(RF_AdaBoost) was proposed. This method uses RF as the weak classifier of AdaBoost to improve the ide.pngication accuracy and enhance the robustness of the model. RF, Extreme Learning Machine(ELM), Kernel Extreme Learning Machine(KELM), Stacked Auto-Encoder(SAE), Back Propagation(BP), Principal Component Analysis and Linear Discriminant Analysis(PCA-LDA), Partial Least Squares Discriminant Analysis(PLS-DA) are used to compare with the RF_AdaBoost model, and the training sets of different scales of blood Raman spectroscopy data were used for ide.pngication experiments to evaluate its performance. With the increase of training samples in the experiment, the ide.pngication accuracy of RF_AdaBoost is up to 100%, and the prediction standard deviation tends to zero. The results show that RF_AdaBoost has higher classification accuracy and stronger stability than other models, which provides an effective new method for the ide.pngication of blood species.

Key words: Raman spectroscopy, Random forest, AdaBoost algorithm, Ensemble learning, Boold species ide.pngication

CLC Number:

O652

TrendMD:

WEI Manman,LU Haoxiang,YANG Huihua. Research on Boold Species Ide.pngication Algorithm Based on RF_AdaBoost Model ^†[J]. Chem. J. Chinese Universities, 2020, 41(1): 94.

Figures/Tables 10

References 19

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Number of sample in the training set	100	200	300	400	500	600	700	800	900
Number of negative sample	50	100	150	200	250	300	350	400	450
Ratio of negative sample	0.100	0.201	0.301	0.402	0.502	0.602	0.703	0.803	0.904
Number of positive sample	50	100	150	200	250	300	350	400	450
Ratio of positive sample	0.093	0.187	0.280	0.374	0.467	0.561	0.654	0.748	0.841

Number of sample in the training set	100	200	300	400	500	600	700	800	900
Number of negative sample	50	100	150	200	250	300	350	400	450
Ratio of negative sample	0.100	0.201	0.301	0.402	0.502	0.602	0.703	0.803	0.904
Number of positive sample	50	100	150	200	250	300	350	400	450
Ratio of positive sample	0.093	0.187	0.280	0.374	0.467	0.561	0.654	0.748	0.841

Train/test data	RF_AdaBoost	RF	SVM	ELM	KELM	SAE	BP	PCA-LDA	PLS-DA
100/933	98.95	89.25	96.46	82.64	72.88	70.31	75.99	74.29	93.05
200/833	99.21	93.77	96.88	91.40	79.66	70.18	75.64	76.70	93.93
300/733	99.58	95.89	98.61	95.21	83.41	71.84	76.86	76.21	93.83
400/633	99.90	96.54	98.34	95.64	88.39	70.32	74.04	76.43	93.94
500/533	99.96	97.39	98.48	96.75	88.87	67.04	72.83	77.65	94.19
600/433	100.00	97.45	98.41	96.65	91.92	71.25	77.14	76.37	94.13
700/333	100.00	97.79	99.55	97.81	93.00	69.85	75.29	78.02	94.24
800/233	100.00	98.08	99.18	97.77	93.26	69.27	74.89	76.91	94.28
900/133	100.00	98.33	99.25	98.20	96.39	70.30	82.26	79.47	94.30

Train/test data	RF_AdaBoost	RF	SVM	ELM	KELM	SAE	BP	PCA-LDA	PLS-DA
100/933	98.95	89.25	96.46	82.64	72.88	70.31	75.99	74.29	93.05
200/833	99.21	93.77	96.88	91.40	79.66	70.18	75.64	76.70	93.93
300/733	99.58	95.89	98.61	95.21	83.41	71.84	76.86	76.21	93.83
400/633	99.90	96.54	98.34	95.64	88.39	70.32	74.04	76.43	93.94
500/533	99.96	97.39	98.48	96.75	88.87	67.04	72.83	77.65	94.19
600/433	100.00	97.45	98.41	96.65	91.92	71.25	77.14	76.37	94.13
700/333	100.00	97.79	99.55	97.81	93.00	69.85	75.29	78.02	94.24
800/233	100.00	98.08	99.18	97.77	93.26	69.27	74.89	76.91	94.28
900/133	100.00	98.33	99.25	98.20	96.39	70.30	82.26	79.47	94.30

Train/test data	Training time/s
Train/test data	RF_AdaBoost	RF	SVM	ELM	KELM	SAE	BP	PCA-LDA	PLS-DA
100/933	21.23	2.07	0.03	0.01	0.04	13.88	28.20	1.03	0.03
200/833	18.88	1.71	0.12	0.01	0.04	25.16	29.85	1.04	0.06
300/733	31.69	3.02	0.14	0.01	0.05	36.63	31.89	1.04	0.10
400/633	92.20	9.11	0.23	0.01	0.05	48.04	34.01	1.04	0.13
500/533	61.65	5.94	0.36	0.02	0.06	59.43	36.18	1.04	0.16
600/433	52.73	7.61	0.51	0.03	0.07	71.75	39.01	1.05	0.19
700/333	64.10	9.35	0.53	0.03	0.07	84.56	41.24	1.05	0.23
800/233	77.07	10.80	0.66	0.04	0.08	95.00	44.32	1.06	0.26
900/133	135.97	16.67	0.75	0.05	0.08	107.02	58.71	1.08	0.29