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KEEL-dataset - data set description
dataset/images/fox.jpg



This section describes main characteristics of the fox data set and its attributes:

General information

Fox data set
TypeMulti instanceOriginReal world
Features 231(Real / Integer / Nominal)(230 / 0 / 1)
Instances1320 Classes2
Missing values?No

Attribute description

AttributeDomainAttributeDomainAttributeDomain
ID-Bag{94, ... , 85}Atr-77[-0.050171, 41.8261]Atr-154[-1.06626, 3.6693]
Atr-1[-1.34546, 3.088]Atr-78[-0.114846, 14.9845]Atr-155[-0.655993, 5.44759]
Atr-2[-1.26716, 4.00391]Atr-79[-0.3317, 9.64399]Atr-156[-0.171326, 9.90494]
Atr-3[-1.76601, 2.16554]Atr-80[-0.408478, 9.10261]Atr-157[-0.072849, 5.17491]
Atr-4[-1.63721, 3.08513]Atr-81[-0.263784, 14.6005]Atr-158[-0.032187, -0.032187]
Atr-5[-0.855511, 6.96691]Atr-82[-0.139878, 24.7056]Atr-159[0.0, 0.0]
Atr-6[-2.01175, 2.52298]Atr-83[0.0, 0.0]Atr-160[0.0, 0.0]
Atr-7[-1.82951, 2.23377]Atr-84[-0.06989, 13.9449]Atr-161[-0.061848, 26.8283]
Atr-8[-1.67172, 3.16989]Atr-85[-0.263975, 8.5906]Atr-162[-0.174962, 13.0011]
Atr-9[-0.867142, 5.20979]Atr-86[-0.785786, 6.22183]Atr-163[-0.192699, 12.6433]
Atr-10[-3.08536, 1.82283]Atr-87[-0.590402, 8.63168]Atr-164[-0.054582, 36.8092]
Atr-11[-4.14519, 3.1266]Atr-88[-0.260113, 9.34919]Atr-165[-0.032023, 7.56542]
Atr-12[-3.53465, 3.37236]Atr-89[-0.102253, 5.02543]Atr-166[0.0, 0.0]
Atr-13[-0.054535, 7.92776]Atr-90[0.0, 0.0]Atr-167[0.0, 0.0]
Atr-14[-0.297912, 13.7627]Atr-91[0.0, 0.0]Atr-168[0.0, 0.0]
Atr-15[-0.261335, 11.7855]Atr-92[-0.040796, 34.0009]Atr-169[-0.015029, 66.8646]
Atr-16[-0.302051, 15.3386]Atr-93[-0.056023, 12.4223]Atr-170[-0.021431, 2.53667]
Atr-17[-0.225084, 16.4917]Atr-94[-0.168633, 5.94144]Atr-171[-0.018157, 0.512343]
Atr-18[-0.014952, -0.014952]Atr-95[-0.188284, 6.73332]Atr-172[0.0, 0.0]
Atr-19[-0.08711, 13.7572]Atr-96[-0.115671, 24.8261]Atr-173[0.0, 0.0]
Atr-20[-0.167402, 12.9821]Atr-97[-0.074486, 24.2595]Atr-174[0.0, 0.0]
Atr-21[-0.200329, 12.4912]Atr-98[0.0, 0.0]Atr-175[0.0, 0.0]
Atr-22[-0.248836, 13.6679]Atr-99[0.0, 0.0]Atr-176[0.0, 0.0]
Atr-23[-0.021302, 55.4048]Atr-100[0.0, 0.0]Atr-177[0.0, 0.0]
Atr-24[-0.028883, -0.028883]Atr-101[-0.014952, 66.8655]Atr-178[-0.019294, -0.019294]
Atr-25[-0.038542, 27.9956]Atr-102[-0.014982, 66.8654]Atr-179[-0.017308, -0.017308]
Atr-26[-0.023332, 0.531869]Atr-103[-0.040764, 18.7335]Atr-180[0.0, 0.0]
Atr-27[-0.023412, 0.135876]Atr-104[-0.042342, 40.0453]Atr-181[0.0, 0.0]
Atr-28[0.0, 0.0]Atr-105[-0.027653, 43.998]Atr-182[0.0, 0.0]
Atr-29[0.0, 0.0]Atr-106[-0.014952, -0.014952]Atr-183[0.0, 0.0]
Atr-30[-0.02702, 55.1665]Atr-107[0.0, 0.0]Atr-184[0.0, 0.0]
Atr-31[-0.198246, 14.2965]Atr-108[0.0, 0.0]Atr-185[0.0, 0.0]
Atr-32[-0.20685, 14.225]Atr-109[0.0, 0.0]Atr-186[0.0, 0.0]
Atr-33[-0.080912, 15.3863]Atr-110[0.0, 0.0]Atr-187[0.0, 0.0]
Atr-34[-0.327191, 7.70347]Atr-111[0.0, 0.0]Atr-188[0.0, 0.0]
Atr-35[-0.347995, 13.6468]Atr-112[-0.021732, -0.021732]Atr-189[0.0, 0.0]
Atr-36[-0.26576, 10.2237]Atr-113[-0.021365, -0.021365]Atr-190[0.0, 0.0]
Atr-37[-0.080866, 29.8607]Atr-114[-0.018, -0.018]Atr-191[0.0, 0.0]
Atr-38[-0.224814, 17.4763]Atr-115[0.0, 0.0]Atr-192[0.0, 0.0]
Atr-39[-0.529019, 10.7837]Atr-116[0.0, 0.0]Atr-193[0.0, 0.0]
Atr-40[-0.451394, 8.64103]Atr-117[0.0, 0.0]Atr-194[0.0, 0.0]
Atr-41[-0.309461, 17.1723]Atr-118[0.0, 0.0]Atr-195[0.0, 0.0]
Atr-42[-0.025787, 61.1147]Atr-119[0.0, 0.0]Atr-196[0.0, 0.0]
Atr-43[-0.073582, 23.3113]Atr-120[0.0, 0.0]Atr-197[0.0, 0.0]
Atr-44[-0.114571, 7.18813]Atr-121[-0.017556, -0.017556]Atr-198[0.0, 0.0]
Atr-45[-0.18949, 20.1713]Atr-122[-0.021126, -0.021126]Atr-199[0.0, 0.0]
Atr-46[-0.160794, 16.5738]Atr-123[0.0, 0.0]Atr-200[0.0, 0.0]
Atr-47[-0.201557, 15.949]Atr-124[0.0, 0.0]Atr-201[-0.032096, 42.7457]
Atr-48[0.0, 0.0]Atr-125[0.0, 0.0]Atr-202[-0.043388, 0.383507]
Atr-49[-0.021708, 56.2576]Atr-126[0.0, 0.0]Atr-203[-0.027179, -0.027179]
Atr-50[-0.021658, 56.2268]Atr-127[0.0, 0.0]Atr-204[0.0, 0.0]
Atr-51[-0.041491, 18.1953]Atr-128[0.0, 0.0]Atr-205[0.0, 0.0]
Atr-52[-0.032279, 1.65132]Atr-129[0.0, 0.0]Atr-206[0.0, 0.0]
Atr-53[-0.071887, 4.36325]Atr-130[0.0, 0.0]Atr-207[-0.099876, 14.2564]
Atr-54[-0.095154, 23.6699]Atr-131[0.0, 0.0]Atr-208[-0.135405, 17.8328]
Atr-55[-0.014952, -0.014952]Atr-132[0.0, 0.0]Atr-209[-0.183154, 7.88095]
Atr-56[0.0, 0.0]Atr-133[0.0, 0.0]Atr-210[-0.106346, 10.0514]
Atr-57[0.0, 0.0]Atr-134[-0.018485, 64.2928]Atr-211[-0.037709, 4.73002]
Atr-58[0.0, 0.0]Atr-135[-0.01594, -0.01594]Atr-212[0.0, 0.0]
Atr-59[0.0, 0.0]Atr-136[0.0, 0.0]Atr-213[0.0, 0.0]
Atr-60[-0.014952, 66.8655]Atr-137[0.0, 0.0]Atr-214[0.0, 0.0]
Atr-61[-0.017265, 7.17122]Atr-138[-0.043134, -0.043134]Atr-215[-0.265739, 8.10841]
Atr-62[-0.024647, -0.024647]Atr-139[-0.029996, -0.029996]Atr-216[-0.746243, 3.66377]
Atr-63[-0.021215, -0.021215]Atr-140[-0.09331, 24.2192]Atr-217[-0.412626, 7.14215]
Atr-64[0.0, 0.0]Atr-141[-0.098605, 17.7319]Atr-218[-0.102856, 12.9679]
Atr-65[0.0, 0.0]Atr-142[-0.071579, 2.44974]Atr-219[-0.017565, 0.542451]
Atr-66[0.0, 0.0]Atr-143[-0.017934, -0.017934]Atr-220[0.0, 0.0]
Atr-67[0.0, 0.0]Atr-144[-0.063725, 25.4317]Atr-221[0.0, 0.0]
Atr-68[0.0, 0.0]Atr-145[-0.182197, 13.0544]Atr-222[0.0, 0.0]
Atr-69[0.0, 0.0]Atr-146[-0.255619, 7.23322]Atr-223[-0.049855, 14.0601]
Atr-70[-0.065785, 32.2483]Atr-147[-0.391468, 7.46106]Atr-224[-0.114025, 20.4391]
Atr-71[-0.049974, 3.36338]Atr-148[-0.187455, 8.27953]Atr-225[-0.078862, 16.5204]
Atr-72[-0.014952, -0.014952]Atr-149[-0.077681, 28.9964]Atr-226[-0.021452, 7.67244]
Atr-73[-0.048732, 10.5359]Atr-150[-0.03269, 44.6083]Atr-227[0.0, 0.0]
Atr-74[-0.173228, 16.0503]Atr-151[0.0, 0.0]Atr-228[0.0, 0.0]
Atr-75[-0.177573, 18.9163]Atr-152[-0.053085, 21.1643]Atr-229[-0.014952, 66.8655]
Atr-76[-0.091775, 11.0413]Atr-153[-0.364504, 8.26899]Atr-230[-0.021097, -0.021097]
Class{0, 1}

Additional information

This problem consist of identifying the intended target object(s) in images. The main difficulty is due to the fact that an image may contain multiple, possibly heterogeneous objects. Thus, the global description of a whole image is too coarse to achieve good classification and retrieval accuracy. Even if relevant images are provided, identifying which object(s) within the example images are relevant remains a hard problem in the supervised learning setting. However, this problem fits in MIL settings well: each image can be treated as a bag of segments which are modeled as instances, and the concept point representing the target object can be learned through MIL algorithms. This data set considers data sets representing foxes. Each data set consists of 100 images which contains foxes and the other 100 images which contains another different animals. The final goal consist of distinguising images containing the foxes from those that do not contain it.




In this section you can download some files related to the fox data set:

  • The complete data set already formatted in KEEL format can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from herezip.gif.
  • The header file associated to this data set can be downloaded from heretxt.png.


 
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