Training set for Random data. If you use this data please quote the following reference. S. Mitra and L. I. Kuncheva, ``Improving classification performance using fuzzy MLP and two-level selective partitioning of the feature space'', Fuzzy Sets and Systems, Vol. 70, pp. 1-13, 1995. No. of pattern points = 200, No. of features = 2 There are two classes. Class F1 F2 1 0.149 0.019 1 0.958 0.512 1 0.528 0.066 1 0.492 0.616 1 0.761 0.409 1 0.669 0.517 1 0.350 0.206 1 0.663 0.385 1 0.152 0.121 1 0.880 0.274 1 0.722 0.583 1 0.668 0.041 1 0.690 0.196 1 0.534 0.280 1 0.433 0.699 1 0.402 0.187 1 0.979 0.233 1 0.469 0.532 1 0.974 0.218 1 0.305 0.487 1 0.297 0.588 1 0.431 0.112 1 0.233 0.299 1 0.309 0.484 1 0.651 0.419 1 0.129 0.355 1 0.729 0.077 1 0.516 0.506 1 0.015 0.148 1 0.064 0.205 1 0.544 0.050 1 0.428 0.270 1 0.467 0.391 1 0.648 0.040 1 0.536 0.229 1 0.420 0.662 1 0.178 0.318 1 0.520 0.069 1 0.625 0.103 1 0.945 0.229 1 0.039 0.455 1 0.453 0.513 1 0.782 0.231 1 0.329 0.074 1 0.330 0.415 1 0.667 0.264 1 0.476 0.585 1 0.186 0.472 1 0.425 0.609 1 0.778 0.125 1 0.666 0.314 1 0.796 0.053 1 0.255 0.469 1 0.416 0.679 1 0.999 0.156 1 0.055 0.309 1 0.780 0.155 1 0.076 0.294 1 0.150 0.494 1 0.378 0.451 1 0.726 0.445 1 0.728 0.429 1 0.709 0.275 1 0.373 0.355 1 0.584 0.243 1 0.834 0.327 1 0.374 0.710 1 0.905 0.044 1 0.252 0.553 1 0.702 0.574 1 0.076 0.105 1 0.122 0.495 1 0.355 0.702 1 0.950 0.189 1 0.047 0.095 1 0.865 0.279 1 0.706 0.629 1 0.052 0.431 1 0.084 0.051 1 0.413 0.416 1 0.439 0.572 1 0.980 0.530 1 0.274 0.579 1 0.314 0.404 1 0.254 0.181 1 0.714 0.397 1 0.252 0.284 1 0.602 0.071 1 0.411 0.550 1 0.875 0.092 1 0.384 0.232 1 0.468 0.455 1 0.154 0.272 1 0.400 0.645 1 0.076 0.152 1 0.410 0.259 1 0.341 0.147 1 0.326 0.250 1 0.986 0.445 1 0.426 0.688 1 0.818 0.171 1 0.800 0.146 1 0.421 0.346 1 0.449 0.398 1 0.857 0.725 1 0.749 0.158 2 0.539 0.769 2 0.720 0.879 2 0.787 0.301 2 0.589 0.615 2 0.928 0.754 2 0.169 0.702 2 0.258 0.643 2 0.048 0.925 2 0.800 0.884 2 0.789 0.571 2 0.157 0.548 2 0.146 0.953 2 0.518 0.596 2 0.124 0.958 2 0.439 0.865 2 0.904 0.854 2 0.976 0.966 2 0.911 0.752 2 0.177 0.712 2 0.944 0.682 2 0.523 0.677 2 0.589 0.726 2 0.618 0.416 2 0.552 0.540 2 0.771 0.894 2 0.809 0.722 2 0.302 0.928 2 0.044 0.613 2 0.255 0.640 2 0.231 0.619 2 0.468 0.810 2 0.733 0.828 2 0.825 0.478 2 0.899 0.571 2 0.805 0.792 2 0.382 0.954 2 0.687 0.721 2 0.698 0.829 2 0.958 0.696 2 0.652 0.963 2 0.398 0.906 2 0.271 0.963 2 0.157 0.648 2 0.496 0.976 2 0.933 0.549 2 0.733 0.818 2 0.779 0.337 2 0.004 0.890 2 0.791 0.732 2 0.070 0.960 2 0.550 0.502 2 0.803 0.436 2 0.237 0.895 2 0.703 0.747 2 0.637 0.399 2 0.586 0.443 2 0.364 0.839 2 0.162 0.988 2 0.560 0.569 2 0.311 0.767 2 0.695 0.869 2 0.357 0.942 2 0.148 0.780 2 0.933 0.931 2 0.525 0.606 2 0.901 0.511 2 0.906 0.613 2 0.598 0.483 2 0.730 0.797 2 0.585 0.492 2 0.906 0.427 2 0.431 0.911 2 0.615 0.458 2 0.478 0.926 2 0.567 0.844 2 0.652 0.612 2 0.204 0.843 2 0.425 0.797 2 0.200 0.901 2 0.795 0.715 2 0.271 0.689 2 0.138 0.560 2 0.882 0.780 2 0.739 0.938 2 0.999 0.850 2 0.383 0.870 2 0.176 0.672 2 0.236 0.957 2 0.845 0.743 2 0.291 0.683 2 0.357 0.885 2 0.783 0.957 2 0.924 0.698 2 0.314 0.843