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 	 0.183
3 	 398 	 aad 	 0.137
4 	 574 	 fdc 	 0.134
5 	 537 	 eed 	 0.133
6 	 305 	 fbb 	 0.125
7 	 502 	 dad 	 0.121
8 	 356 	 ffe 	 0.121
9 	 541 	 eec 	 0.115
10 	 431 	 cde 	 0.109
11 	 591 	 bcb 	 0.101
12 	 531 	 add 	 0.094
13 	 278 	 bde 	 0.092
14 	 420 	 fca 	 0.089
15 	 319 	 bgb 	 0.088
16 	 551 	 dcg 	 0.087
17 	 306 	 fba 	 0.087
18 	 280 	 abb 	 0.086
19 	 545 	 dda 	 0.085
20 	 503 	 dae 	 0.083
21 	 419 	 bdg 	 0.080
22 	 355 	 fff 	 0.080
23 	 275 	 cfb 	 0.078
24 	 379 	 bae 	 0.078
25 	 366 	 ceb 	 0.078
26 	 290 	 dcc 	 0.076
27 	 287 	 cgc 	 0.075
28 	 507 	 dab 	 0.074
29 	 411 	 efa 	 0.074
30 	 359 	 ffb 	 0.074
31 	 434 	 cda 	 0.072
32 	 390 	 dfe 	 0.071
33 	 597 	 bca 	 0.070
34 	 535 	 ada 	 0.069
35 	 308 	 fbf 	 0.068
36 	 344 	 afb 	 0.067
37 	 538 	 eeg 	 0.067
38 	 529 	 adf 	 0.066
39 	 339 	 afd 	 0.063
40 	 285 	 abg 	 0.063
41 	 557 	 cgf 	 0.063
42 	 519 	 cca 	 0.063
43 	 504 	 daf 	 0.062
44 	 407 	 bef 	 0.062
45 	 532 	 ade 	 0.061
46 	 410 	 bee 	 0.061
47 	 313 	 fed 	 0.061
48 	 371 	 gfc 	 0.061
49 	 438 	 dbc 	 0.061
50 	 330 	 def 	 0.060
51 	 353 	 egd 	 0.059
52 	 534 	 adc 	 0.058
53 	 573 	 fda 	 0.057
54 	 288 	 dce 	 0.055
55 	 289 	 dcb 	 0.054
56 	 472 	 baa 	 0.054
57 	 283 	 abe 	 0.054
58 	 345 	 afc 	 0.053
59 	 274 	 cfe 	 0.052
60 	 527 	 gdf 	 0.052
61 	 444 	 bfc 	 0.051
62 	 271 	 cff 	 0.050
63 	 334 	 gcb 	 0.050
64 	 555 	 cgd 	 0.049
65 	 418 	 aba 	 0.049
66 	 368 	 dac 	 0.048
67 	 378 	 aec 	 0.047
68 	 307 	 fbg 	 0.047
69 	 263 	 ede 	 0.047
70 	 257 	 edb 	 0.046
71 	 517 	 ccg 	 0.046
72 	 421 	 fcb 	 0.046
73 	 456 	 gee 	 0.045
74 	 580 	 bcd 	 0.045
75 	 463 	 gbc 	 0.044
76 	 373 	 gff 	 0.044
77 	 543 	 ddc 	 0.044
78 	 311 	 fef 	 0.044
79 	 428 	 abf 	 0.043
80 	 436 	 cdc 	 0.042
81 	 583 	 cbb 	 0.042
82 	 481 	 fge 	 0.040
83 	 582 	 bcf 	 0.040
84 	 365 	 cec 	 0.040
85 	 550 	 dcf 	 0.040
86 	 266 	 faa 	 0.040
87 	 331 	 deg 	 0.039
88 	 391 	 dfd 	 0.039
89 	 560 	 aca 	 0.039
90 	 265 	 fac 	 0.039
91 	 539 	 eef 	 0.039
92 	 349 	 ega 	 0.037
93 	 482 	 fgf 	 0.037
94 	 501 	 cab 	 0.037
95 	 569 	 eae 	 0.036
96 	 322 	 bgf 	 0.036
97 	 304 	 fbc 	 0.035
98 	 553 	 cgb 	 0.035
99 	 361 	 ceg 	 0.035
100 	 413 	 efc 	 0.035
101 	 558 	 acc 	 0.034
102 	 321 	 bge 	 0.033
103 	 258 	 edc 	 0.033
104 	 269 	 fad 	 0.032
105 	 588 	 cbd 	 0.032
106 	 576 	 fde 	 0.032
107 	 417 	 efg 	 0.032
108 	 484 	 fga 	 0.032
109 	 521 	 ccb 	 0.032
110 	 367 	 cea 	 0.031

*** mRMR features *** 
Order 	 Fea 	 Name 	 Score
1 	 536 	 eee 	 0.258
2 	 549 	 ddd 	 0.087
3 	 551 	 dcg 	 0.049
4 	 319 	 bgb 	 0.040
5 	 356 	 ffe 	 0.027
6 	 574 	 fdc 	 0.032
7 	 538 	 eeg 	 0.029
8 	 541 	 eec 	 0.034
9 	 591 	 bcb 	 0.035
10 	 285 	 abg 	 0.031
11 	 431 	 cde 	 0.032
12 	 419 	 bdg 	 0.026
13 	 398 	 aad 	 0.026
14 	 306 	 fba 	 0.025
15 	 373 	 gff 	 0.024
16 	 287 	 cgc 	 0.026
17 	 537 	 eed 	 0.026
18 	 353 	 egd 	 0.027
19 	 305 	 fbb 	 0.030
20 	 502 	 dad 	 0.027
21 	 290 	 dcc 	 0.027
22 	 420 	 fca 	 0.026
23 	 344 	 afb 	 0.023
24 	 527 	 gdf 	 0.022
25 	 289 	 dcb 	 0.020
26 	 417 	 efg 	 0.018
27 	 355 	 fff 	 0.020
28 	 545 	 dda 	 0.020
29 	 288 	 dce 	 0.019
30 	 280 	 abb 	 0.019
31 	 371 	 gfc 	 0.015
32 	 484 	 fga 	 0.014
33 	 482 	 fgf 	 0.014
34 	 278 	 bde 	 0.015
35 	 379 	 bae 	 0.014
36 	 334 	 gcb 	 0.012
37 	 366 	 ceb 	 0.011
38 	 556 	 cgg 	 0.011
39 	 529 	 adf 	 0.010
40 	 456 	 gee 	 0.011
41 	 349 	 ega 	 0.011
42 	 454 	 geg 	 0.010
43 	 308 	 fbf 	 0.010
44 	 463 	 gbc 	 0.010
45 	 359 	 ffb 	 0.010
46 	 361 	 ceg 	 0.009
47 	 531 	 add 	 0.009
48 	 117 	 CCGT 	 0.008
49 	 275 	 cfb 	 0.009
50 	 307 	 fbg 	 0.007
51 	 503 	 dae 	 0.007
52 	 517 	 ccg 	 0.006
53 	 351 	 egf 	 0.006
54 	 297 	 ggb 	 0.006
55 	 390 	 dfe 	 0.006
56 	 532 	 ade 	 0.006
57 	 376 	 gfe 	 0.006
58 	 339 	 afd 	 0.005
59 	 447 	 bfg 	 0.004
60 	 411 	 efa 	 0.005
61 	 45 	 TAAA 	 0.004
62 	 519 	 cca 	 0.005
63 	 214 	 AGCC 	 0.004
64 	 526 	 gde 	 0.004
65 	 646 	 length_ratio 	 0.003
66 	 494 	 gag 	 0.003
67 	 191 	 GTTC 	 0.003
68 	 407 	 bef 	 0.004
69 	 597 	 bca 	 0.003
70 	 87 	 AGGT 	 0.002
71 	 573 	 fda 	 0.003
72 	 345 	 afc 	 0.002
73 	 410 	 bee 	 0.003
74 	 320 	 bgd 	 0.002
75 	 272 	 cfg 	 0.002
76 	 131 	 CTCG 	 0.002
77 	 434 	 cda 	 0.002
78 	 17 	 TAGC 	 0.002
79 	 55 	 TCTC 	 0.001
80 	 265 	 fac 	 0.001
81 	 159 	 TTTC 	 0.001
82 	 150 	 ATTG 	 0.001
83 	 444 	 bfc 	 0.001
84 	 274 	 cfe 	 0.001
85 	 53 	 ACTA 	 0.001
86 	 513 	 dgd 	 0.001
87 	 438 	 dbc 	 0.001
88 	 483 	 fgg 	 0.000
89 	 1 	 GTAC 	 0.000
90 	 271 	 cff 	 0.000
91 	 123 	 TGGA 	 0.000
92 	 257 	 edb 	 0.001
93 	 252 	 TCCG 	 0.000
94 	 534 	 adc 	 0.001
95 	 24 	 GAAC 	 0.000
96 	 507 	 dab 	 -0.000
97 	 169 	 TGAC 	 -0.000
98 	 91 	 TTCC 	 -0.000
99 	 180 	 TATC 	 -0.000
100 	 313 	 fed 	 0.000
101 	 283 	 abe 	 -0.000
102 	 639 	 G_pos 	 -0.000
103 	 52 	 TCTG 	 -0.000
104 	 337 	 gcg 	 -0.000
105 	 224 	 CACT 	 -0.000
106 	 472 	 baa 	 -0.000
107 	 111 	 GCCT 	 -0.000
108 	 263 	 ede 	 -0.000
109 	 350 	 egg 	 -0.000
110 	 330 	 def 	 -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.