Assignment_1.pdf - cnico Lisboa Te Intelligent Systems...

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T ´ ecnico Lisboa Intelligent Systems Master’s Degree in Mechanical Engineering Assigment 1 Written by: Ricardo Lopes - 75184 Professors: Susana Vieira Jo˜ ao Sousa 2017
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Assignment 1 75184 Dataset The dataset chosen for this assigment is from an E. Coli diagnosis problem. The dataset has 336 samples with 7 attributes each. 1. Mcg R [0 . 0 , 0 . 89] 2. Gvh R [0 . 16 , 1 . 0] 3. Lip R [0 . 48 , 1 . 0] 4. Chg R [0 . 5 , 1 . 0] 5. Aac R [0 . 0 , 0 . 88] 6. Alm1 R [0 . 03 , 1 . 0] 7. Alm2 R [0 . 0 , 0 . 99] This 7 attributes are used to diagnose E. Coli, giving an output of a positive diag- nosis or a negative one. To facilitate data treatment, all positive diagnosis are considered a 1 , and the neg- ative a 0 . In total there are 77 positive diagnosis and 259 negative ones. Figure 1: Negative and Positive diagnosis distribution in the Dataset Intelligent Systems 1
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Assignment 1 75184 Model The function genfis3 from MATLAB’s Fuzzy Toolbox was used to generate a Takagi- Sugeno Fuzzy Interference System (FIS). The genfis3 function uses fuzzy c-means clus- tering to extract memberships functions from the data.
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  • Spring '16
  • Type I and type II errors, Intelligent Systems, Fuzzy Toolbox

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