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 <hanchuan.peng@gmail.com>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.