CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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The classification results of the standalone and integrated datasets of case study 2 are presented in Figure 5-7 as percentages of correctly classified samples ( ). In the case of FTIR, the overall accuracy obtained by the RBF SVMs is extremely low compared to the other two linear techniques. It can be therefore concluded that the projection of the data into a high dimensional space by the kernel-based SVM is unsuitable in this instance. On the contrary, PLS-DA achieves 66%, the highest overall accuracy among the three techniques; as stated in Section 1.5.2, this result is justified by the fact that PLS-DA uses all available samples instead of a limited number of samples (the support vectors) to create the separation boundaries (Boser et al., 1992; Brereton et al., 2009; Smolinska et al., 2012); thus, it is less prone to be dominated by the majority class and the highly imbalanced classes. Furthermore, when the FTIR data were subjected to PCA, the overall accuracy of PLS-DA increased by 3% and by at least 10% in the case of SVMs. Even though PCA notably enhances the overall performance of all implemented classifiers, linear SVMs once more take the lead among the three classification ensembles. Both linear and RBF SVMs produce an overall accuracy equal to 71% for the HPLC data, which is significantly higher than the overall accuracy of 67% obtained by PLS-DA. According to Section 1.5.2.3, the performance of a nonlinear SVM can be at least as good as the linear SVM (Boser et al., 1992; Keerthi and Lin, 2003; Hsu et al., 2003; Chang et al., 2010); thus, suitable combinations of the RBF hyperparameters ( and ) have formed boundaries that tend towards linearity and hence result in equal overall accuracies between the linear and nonlinear SVMs (Figure 5-7). In addition, the application of PCA has proven extremely fruitful in the case of nonlinear SVMs, where the highest recorded accuracy, equal to 73%, is observed. For case study 2, data integration was not as fruitful as expected. The documented achieved by both GPA and CPCA do not exceed the highest recorded accuracy of 73% by standalone HPLC. However, in the case of CPCA and the linear classifiers (PLS-DA and linear SVMs), the overall accuracies of the integrated datasets are at least as good as those acquired by the standalone datasets. In the case of GPA, all results for the integrated dataset are lower than those of the individual datasets. This 125
observation confirms that indeed CPCA is a better data fusion technique compared to GPA. Figure 5-7 Overall accuracies (%CC) for the standalone and integrated datasets of case study 2 The figure illustrates the overall performance of all implemented classification ensembles on the standalone and integrated datasets of case study 2. The bars represent the percentages of correctly classified samples (%CC) and are coloured according to the classification model under study (PLS-DA, linear and RBF SVMs). In the case of standalone datasets, analyses have been conducted both prior (raw data) and after PCA. Data integration has been performed using both Generalized Procrustes Analysis (GPA) and Consensus PCA (CPCA). In all implemented classifiers, bootstrapping was applied for hyperparameter optimisation. The overall accuracies have been rounded towards the nearest integer. 126
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CRANFIELD UNIVERSITY Eleni Anthippi
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ABSTRACT Muscle foods such as meat,
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TABLE OF CONTENTS ABSTRACT ........
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6.2.4 The iWebPlots package .......
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Figure 3-12 Speedup produced by the
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Figure 6-9 Sweave example for the d
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TABLE OF EQUATIONS Equation 1 Mean-
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RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
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Figure 1-1 Evolution from molecular
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1.1.2.1 Fourier Transform Infrared
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1.1.3 Microbial Spoilage in Meat Sy
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The multivariate techniques applied
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1.4 Multivariate Analysis: Unsuperv
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1.4.2 Cluster Analysis Cluster anal
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1.5 Multivariate Analysis: Supervis
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In any linearly separable binary da
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Where ( ) are the Lagrange multipli
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Every kernel is characterised by a
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The “one-against-all” approach
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Furthermore, metrics such as the bi
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1.6.3 Leave-One-Out Cross-Validatio
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In such cases, the model becomes qu
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1.8 Aims and objectives The overall
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2 Development of the multivariate a
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Sensory panel scores were of the hi
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DF (Hz) 2.2.1.5 Electronic Nose (e-
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Figure 2-3 Data intersection The fi
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3. Validation Techniques In the cas
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Figure 2-4 The process of construct
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2.3 Results and Discussion 2.3.1 Pr
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(b) HPLC data (c) e-nose data Figur
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In the case of FTIR, linear classif
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2.3.2.2 Class Prediction Accuracies
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Figure 2-7 Class prediction rates o
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The topology of the three-dimension
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Figure 2-9 Three-dimensional error
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Furthermore, a thorough comparison
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In the master/slave architecture, a
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precision of the classification acc
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Figure 3-3 The steps of the Nelder-
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3.3 Results and Discussion 3.3.1 Li
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3.3.2 Nonlinear Models In order to
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9) 10) 11) 12) 13) 14) 15) 16) Figu
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As an additional visual aid, these
Page 89 and 90: a) Number of iterations b) Number o
Page 91 and 92: Figure 3-10 Comparison of the execu
Page 93 and 94: Figure 3-12 Speedup produced by the
Page 95 and 96: 4 Integration of Heterogeneous Data
Page 97 and 98: 4.2.2 Procrustes Analysis Procruste
Page 99 and 100: symmetric method since the ordering
Page 101 and 102: Figure 4-3 Steps of CPCA for the da
Page 103 and 104: 4.2.5 Data Integration and Analysis
Page 105 and 106: Figure 4-4 Data integration workflo
Page 107 and 108: a) GPA b) CPCA Figure 4-6 The conse
Page 109 and 110: accuracies as the linear models; po
Page 111 and 112: Figure 4-8 Classification Results f
Page 113 and 114: Similar to FTIR, the PLS-DA ensembl
Page 115 and 116: Figure 4-9 Class prediction rates o
Page 117 and 118: 4.3.3 Permutation Tests Even though
Page 119 and 120: Even though the interpretation of t
Page 121 and 122: Figure 4-12 Distribution plots of t
Page 123 and 124: RBF SVMs Datasets Original %CC Mean
Page 125 and 126: Figure 4-14 Boxplots representing t
Page 127 and 128: 5 Application of the multivariate a
Page 129 and 130: Figure 5-1 Mean FTIR spectra for ca
Page 131 and 132: 5.2.2.2 Fourier Transform Infrared
Page 133 and 134: 5.2.2.4 Data Overview For each expe
Page 135 and 136: 5.2.3.3 High Throughput Liquid Chro
Page 137 and 138: Figure 5-5 displays the PCA scores
Page 139: (a) GPA (b) CPCA Figure 5-6 The con
Page 143 and 144: Figure 5-8 Class prediction rates o
Page 145 and 146: (a) RBF SVMs (b) PLS-DA Figure 5-9
Page 149 and 150: Figure 5-12 Execution times of the
Page 151 and 152: (a) FTIR data (b) Raman data Figure
Page 153 and 154: The classification results for the
Page 155 and 156: Figure 5-16 Class prediction rates
Page 157 and 158: (a) RBF SVMs (b) PLS-DA Figure 5-17
Page 161 and 162: Figure 5-20 Execution times of the
Page 163 and 164: (a) FTIR data (b) HPLC data 148
Page 165 and 166: (a) GPA (b) CPCA Figure 5-22 The co
Page 167 and 168: The classification results of the f
Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
Page 173 and 174: The permutation results for the dat
Page 175 and 176: Figure 5-28 Distribution plots of t
Page 179 and 180: Figure 5-30 Boxplots representing t
Page 181 and 182: 5.4 Comparison of the individual ca
Page 183 and 184: Figure 5-32 Investigating the commo
Page 185 and 186: 6 Development of improved visualisa
Page 187 and 188: 6.2.2 Generating static graphs As d
Page 189 and 190: Figure 6-2 Construction process of
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6.2.2.2 Generating dynamic reports
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activity, while simultaneously the
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or disappeared (Wang et al., 2008).
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ectangles, circles and/or irregular
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In addition to the static figures g
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c) graphics package d) ggplot2 pack
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In addition to aesthetics, faceting
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Datasets FTIR HPLC e-nose PLS-DA (L
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Figure 6-10 Interactive dendrogram
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6.4 Conclusion The work reported in
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large number of individual models i
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The generated suite of tools, as pr
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In addition, the difficulty to corr
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REFERENCES Almeida, J. A. S., Barbo
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Processing., Proceedings of the 12t
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Clarke, B., Fokoué, E. and Zhang,
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spectroscopy and machine learning",
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Gower, J. C. (1975), "Generalized p
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Jardon, M. (2006), Systems Biology:
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Nicolaou, N., Xu, Y. and Goodacre,
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Shah, A. A., Barthel, D., Lukasiak,
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Vera, G., Jansen, R. and Suppi, R.
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Xu, Y. and Goodacre, R. (2012), "Mu
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