# A Two-Phase Approach for Semi-Supervised Feature Selection
## 5 Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Dataset and Data Label (in \%)} & \multicolumn{2}{c}{**Control Label**} & \multicolumn{2}{c}{**Control Unlabeled**} & \multirow{2}{*}{**Pairs (Unlabeled, Label)**} & \multirow{2}{*}{**Match**} & \multicolumn{2}{c}{**Min. Distance between**} \\  & **Cluster** & **Control Values** & **Cluster** & **Control Values** & **Label)** & **\%** & **Identification Controls** \\ \hline \multirow{8}{*}{ Thyroid} & 70 & 11.422,2.108 & 1: 1.4782,435 & (1.1) & & 0.3337 \\  & 70 & 22.991,015,311 & 2: 10.083,490,347 & (2.3) & 78.13 & 22.2055 \\  & 31.713,343,082 & 3: 7.711,155,596 & (0.2) & & 6.3334 \\ \cline{2-6}  & 50 & 13.103,024,564 & 1: 9.942,015,052 & (2.2) & & 11.8828 \\  & 50 & 21.559,211 & 2: 9.928,014,233 & (2.1) & 76.64 & 0.2547 \\  & 3.313,321,370,055 & 3: 1.332,377 & (0.3) & & 22.3066 \\ \cline{2-6}  & 40 & 11.286,147,08 & 1: 0.747,193,644 & (1.1) & & 4.9552 \\  & 40 & 22.609,537,518 & 2: 1.712,138,309 & (0.3) & 85.27 & 34.0811 \\  & 32.121,140 & 3: 4.672,220 & (0.2) & & 0.4110 \\ \hline \multirow{8}{*}{Pima} & 70 & 12.154,906,34,164 & 1: 1.858,934,378 & (1.1) & 76.52 & 1.0926 \\  & 40 & 21.012,363,381 & 2: 100.07,31,197 & (0.2) & 2.52 & 1.4661 \\ \cline{2-6}  & 50 & 13.153,935,303 & 1: 15.252,133,366 & (1.1) & 75.26 & 2.5846 \\  & 50 & 22.011,123,302,301 & 2: 100.049,095 & (2.2) & 75.26 & 0.09245 \\ \cline{2-6}  & 40 & 14.679,703,644 & 1: 100.336,299,902 & (0.2) & 76.57 & 9.1445 \\  & 21.020,520,230,236 & 2: 128.792,2816 & (2.1) & & 1.7712 \\ \hline \multirow{8}{*}{Sourar} & 70 & 11.040,073,190,189,129 & 1: 10.234,035,306,194,193,139 & (1.2) & 0.229.0939 \\  & 2.208,030,370,370,257,202 & 2: 10.317,046,180,170,130 & (2.1) & 75.81 & 0.3288,01110 \\ \cline{2-6}  & 50 & 11.142,173,194,192,208 & 10: 12.036,030,370,170,102 & (0.2) & 71.15 & 0.2842,0330 \\ \cline{2-6}  & 22.029,030,365,307,391,149 & 1: 10.237,015,303,130,09 & (2.1) & & 0.0370,032,3211 \\ \cline{2-6}  & 40 & 19.140,159,187,122,039 & 10: 10.385,347,336,197,109,062 & (0.2) & 68 & 0.3097,00353 \\ \hline \multirow{8}{*}{Iones} & & 12.088,048,344,343 & 1: 0.018,999,023,048 & & & \\  & & -20.000,402,227 & 0: 0.020,044,04 & (2.1) & 63.81 & 0.2821 \\  & 21.038,048,199 & 2: -0.4158,048,469 & (2.1) & & 0.3352 \\  & 0.170, 0.226 & -0.369,023,03 & & & \\ \cline{2-6}  & 50 & 11.765,074,073,-0.024, & 1: 0.734,035,016, & & & \\ \cline{2-6}  & 50 & 0.739,094,035 & 0.751,-0.040, & (1.1) & 67.43 & 0.0237 \\  & 2.0175, 0.1975,-253, & 2: -0.115,-0.177,-0.021, & (2.2) & & 0.2908 \\ \cline{2-6}  & 0.275, 0.080, & 0.154,-0.018 & & & \\ \hline \multirow{8}{*}{40} & 1.000,02,078,076, & 1:0.000,0276, 073, & & & \\  & 0.000,003,084 & -0.020,-0.039, & (1.1) & 72.51 & 0.1047 \\ \cline{1-1}  & 20.000,001,641,-0.005, & 2: 0.000,-0.026,-0.213, & (2.2) & 72.51 & 0.1799 \\ \cline{1-1}  & -0.198, 0.082 & -0.181,027 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._For Tables 8 and 9, notations for the methods are explained as follows:  
The methods P1-P9 are summarized in Section 3. *P1-Basic PNN
*P2-RCPNN with gradient descent
*P3-RCPNN with PSO
*P4-Condensed PNN with swarm intelligence
*P5-All PDs with 50% training in proposed scheme
*P6-All PDs with 80% training in proposed scheme
*P7-Only best 50% PDs with 50% training in proposed scheme
*P8-Only best 50% PDs with 80% training in proposed scheme
*P9-Unsupervised method using Sammon's Stress Function
*P10-Proposed method with 70% known labels
*P11-Proposed method with 50% known labels
*P12-Proposed method with 40% known labelsmethod as it is obvious that the results for synthetic data would yield maximum accuracy. For all real datasets, various ratios of the number of labeled patterns vs. number of unlabeled patterns (shown in percent) are shown in Table 2. It is apparent from this table that all experiments have been performed on three sizes of datasets in terms of a known and unknown number of patterns: (70,30), (50,50), and (40,60). Table 6 presents results obtained from experiments with various parameters. Column 1 shows the name of the dataset.