You have specified parameters: threshold=mu+/-0.20*sigma #fea=110 selection method=MID #maxVar=10000 #maxSample=12408 Target classification variable (#1 column in the input data) has name=class entropy score=1.000 *** MaxRel features *** Order Fea Name Score 1 536 eee 0.258 2 549 ddd 0.191 3 537 eed 0.143 4 398 aad 0.136 5 574 fdc 0.134 6 356 ffe 0.131 7 502 dad 0.121 8 305 fbb 0.120 9 541 eec 0.115 10 557 cgf 0.112 11 431 cde 0.109 12 319 bgb 0.099 13 278 bde 0.098 14 531 add 0.093 15 551 dcg 0.091 16 306 fba 0.091 17 420 fca 0.090 18 280 abb 0.089 19 591 bcb 0.087 20 419 bdg 0.086 21 287 cgc 0.083 22 275 cfb 0.080 23 503 dae 0.079 24 507 dab 0.077 25 379 bae 0.077 26 355 fff 0.076 27 434 cda 0.075 28 597 bca 0.075 29 290 dcc 0.073 30 411 efa 0.073 31 366 ceb 0.072 32 535 ada 0.071 33 390 dfe 0.069 34 339 afd 0.069 35 359 ffb 0.068 36 438 dbc 0.067 37 504 daf 0.067 38 519 cca 0.066 39 538 eeg 0.066 40 285 abg 0.066 41 308 fbf 0.065 42 330 def 0.064 43 410 bee 0.064 44 313 fed 0.064 45 371 gfc 0.064 46 545 dda 0.063 47 407 bef 0.063 48 532 ade 0.062 49 344 afb 0.062 50 283 abe 0.058 51 288 dce 0.057 52 534 adc 0.057 53 274 cfe 0.057 54 257 edb 0.056 55 353 egd 0.056 56 527 gdf 0.055 57 444 bfc 0.053 58 418 aba 0.052 59 334 gcb 0.052 60 573 fda 0.052 61 529 adf 0.052 62 368 dac 0.051 63 289 dcb 0.051 64 345 afc 0.051 65 555 cgd 0.050 66 472 baa 0.050 67 463 gbc 0.049 68 517 ccg 0.049 69 580 bcd 0.048 70 307 fbg 0.048 71 543 ddc 0.047 72 456 gee 0.047 73 263 ede 0.045 74 311 fef 0.045 75 271 cff 0.045 76 428 abf 0.044 77 421 fcb 0.044 78 550 dcf 0.043 79 349 ega 0.043 80 378 aec 0.043 81 501 cab 0.042 82 436 cdc 0.042 83 539 eef 0.042 84 322 bgf 0.041 85 365 cec 0.041 86 560 aca 0.041 87 481 fge 0.040 88 373 gff 0.040 89 583 cbb 0.040 90 331 deg 0.039 91 391 dfd 0.039 92 265 fac 0.039 93 558 acc 0.038 94 266 faa 0.038 95 413 efc 0.037 96 576 fde 0.036 97 569 eae 0.036 98 482 fgf 0.035 99 304 fbc 0.035 100 361 ceg 0.035 101 553 cgb 0.034 102 484 fga 0.034 103 258 edc 0.033 104 589 cbe 0.033 105 520 ccc 0.033 106 521 ccb 0.032 107 582 bcf 0.032 108 442 dbg 0.032 109 341 aff 0.031 110 269 fad 0.031 *** mRMR features *** Order Fea Name Score 1 536 eee 0.258 2 549 ddd 0.091 3 551 dcg 0.053 4 319 bgb 0.049 5 356 ffe 0.031 6 538 eeg 0.030 7 574 fdc 0.040 8 541 eec 0.031 9 285 abg 0.031 10 431 cde 0.035 11 419 bdg 0.026 12 537 eed 0.030 13 373 gff 0.026 14 287 cgc 0.026 15 420 fca 0.030 16 398 aad 0.031 17 591 bcb 0.028 18 527 gdf 0.026 19 305 fbb 0.028 20 502 dad 0.026 21 353 egd 0.027 22 306 fba 0.025 23 290 dcc 0.023 24 289 dcb 0.023 25 344 afb 0.020 26 278 bde 0.022 27 288 dce 0.017 28 417 efg 0.017 29 355 fff 0.016 30 456 gee 0.015 31 371 gfc 0.014 32 280 abb 0.014 33 484 fga 0.014 34 557 cgf 0.013 35 349 ega 0.013 36 482 fgf 0.012 37 454 geg 0.012 38 334 gcb 0.012 39 366 ceb 0.011 40 463 gbc 0.012 41 379 bae 0.010 42 556 cgg 0.010 43 275 cfb 0.009 44 361 ceg 0.009 45 503 dae 0.009 46 117 CCGT 0.007 47 307 fbg 0.007 48 545 dda 0.008 49 529 adf 0.008 50 517 ccg 0.008 51 532 ade 0.006 52 308 fbf 0.007 53 376 gfe 0.006 54 531 add 0.006 55 339 afd 0.006 56 526 gde 0.006 57 519 cca 0.005 58 447 bfg 0.005 59 646 length_ratio 0.005 60 411 efa 0.006 61 351 egf 0.004 62 359 ffb 0.005 63 297 ggb 0.005 64 214 AGCC 0.004 65 390 dfe 0.005 66 257 edb 0.004 67 187 CGTC 0.003 68 597 bca 0.003 69 494 gag 0.003 70 191 GTTC 0.002 71 444 bfc 0.002 72 272 cfg 0.002 73 53 ACTA 0.002 74 410 bee 0.002 75 407 bef 0.002 76 513 dgd 0.002 77 133 TCGT 0.001 78 345 afc 0.002 79 24 GAAC 0.001 80 434 cda 0.002 81 180 TATC 0.001 82 438 dbc 0.001 83 17 TAGC 0.001 84 320 bgd 0.001 85 274 cfe 0.001 86 573 fda 0.001 87 131 CTCG 0.001 88 150 ATTG 0.001 89 169 TGAC 0.001 90 283 abe 0.001 91 123 TGGA 0.000 92 209 GAGT 0.000 93 330 def 0.000 94 33 ATAC 0.000 95 483 fgg 0.000 96 576 fde 0.000 97 220 TTGA 0.000 98 271 cff 0.001 99 252 TCCG 0.000 100 507 dab 0.001 101 190 GTTA -0.000 102 534 adc 0.000 103 623 2mer_3 -0.000 104 337 gcg -0.000 105 313 fed -0.000 106 96 GATA -0.000 107 265 fac -0.000 108 350 egg -0.000 109 66 CTCT -0.000 110 580 bcd -0.000 *** This program and the respective minimum Redundancy Maximum Relevance (mRMR) algorithm were developed by Hanchuan Peng for the paper "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy," Hanchuan Peng, Fuhui Long, and Chris Ding, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.