All Tables

Table 1: The sources and numbers of light curves NLC used to define the classes, their average total time span $\langle T_{\rm tot}\rangle$ and their average number of measurements $\langle N_{\rm points}\rangle$ .
Table 2: Stellar variability classes considered in this study, their code, the number of light curves we used (NLC) and their source. Also listed (when relevant for the class) are the ranges for the parameters $T_{\rm eff}$ and $\log g$ if they could be determined from the literature. The last two columns list the range for the dominant frequencies (f₁) and their amplitudes (A₁₁) present in the light curves, resulting from our analysis (Sect. 2.1).
Table 3: The Confusion Matrix for the Gaussian Mixture method, using 25variability classes and 12 classification attributes. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately. The average correct classification rate is about $69\%$ .
Table 4: The confusion matrix for the Gaussian mixture method using 14variability classes and 28 classification attributes. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately. The average correct classification rate is about $92\%$ .
Table 5: The confusion matrix for the Bayesian model averaging of artificial neural networks. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately as measured by 10 fold cross validation.
Table 6: The confusion matrix for the Bayesian model averaging of artificial neural networks and the two class problem. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately as measured by 10-fold cross validation. Separation between: A: eclipsing binaries (ECL) and all other types; B: Cepheids (CEP) and all other types; C: long period variables (LPV) and all other types except ECL and CEP; D: RR Lyrae stars (RR) from all other types except ECL, CEP and LPV.
Table 7: The confusion matrix for the Bayesian model averaging of artificial neural networks. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately as measured by 10-fold cross validation. Separation between: A: Cepheids; B: long period variables; C: RR Lyrae stars.
Table 8: The confusion matrix for the Bayesian model averaging of artificial neural networks for the variables not assigned to any group. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately as measured by 10-fold cross validation.
Table 9: The complete confusion matrix for the Bayesian model averaging of artificial neural networks. The last but one line lists the total number of light curves (TOT) to define every class. The last line lists the correct classification rate (CC) for every class separately as measured by 10-fold cross validation.