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Table 5 Performance of the "ours+SVM" model with incremental feature sets

From: On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach

Min #Inst. per Relation #Uniq. Relation #Instances Methods Per-Relation Per-Instance
  Pos Neg Pos Neg   TP FP Precision Recall F-Score TP FP Precision Recall F-Score
1 618 2,312 1,000 4,834 ours+SVM 152 44 0.776 0.246 0.374 172 67 0.720 0.172 0.278
      +dep. len. 149 35 0.810 0.241 0.371 170 52 0.766 0.170 0.278
      +dist. 150 39 0.794 0.243 0.372 171 63 0.731 0.171 0.277
      +both 151 38 0.799 0.244 0.374 173 58 0.749 0.173 0.281
2 197 695 579 2,827 ours+SVM 94 36 0.723 0.477 0.575 116 63 0.648 0.200 0.306
      +dep. len. 94 32 0.746 0.477 0.582 117 53 0.688 0.202 0.312
      +dist. 92 35 0.724 0.467 0.568 117 54 0.684 0.202 0.312
      +both 94 32 0.746 0.477 0.582 116 51 0.695 0.200 0.311
3 89 314 363 1,835 ours+SVM 58 22 0.725 0.652 0.687 78 32 0.709 0.215 0.330
      +dep. len. 57 22 0.722 0.640 0.679 76 31 0.710 0.209 0.323
      +dist. 57 22 0.722 0.640 0.679 75 31 0.708 0.207 0.320
      +both 57 22 0.722 0.640 0.679 76 31 0.710 0.209 0.323
4 51 181 249 1,349 ours+SVM 38 15 0.717 0.745 0.731 55 25 0.688 0.221 0.335
      +dep. len. 36 13 0.735 0.706 0.720 52 19 0.732 0.209 0.325
      +dist. 38 15 0.717 0.745 0.731 55 25 0.688 0.221 0.335
      +both 36 13 0.735 0.706 0.720 52 19 0.732 0.209 0.325
5 28 107 157 984 ours+SVM 25 12 0.676 0.893 0.769 40 18 0.690 0.255 0.372
      +dep. len. 25 11 0.694 0.893 0.781 40 15 0.727 0.255 0.378
      +dist. 25 12 0.676 0.893 0.769 40 18 0.690 0.255 0.372
      +both 25 11 0.694 0.893 0.781 40 15 0.727 0.255 0.378
  1. 1Note that the result shown here is different from the ones reported in [6]. It may be due to the differences in SVM optimization parameters used for the experiments. We obtained the codes from the authors' web page at http://staff.science.uva.nl/ b ̃ ui/PPIs.zip and ran as is with the parameters: RBF kernel gamma - 0.0145; C = 9; Weka Cost-SensitiveClassifier optimization.
  2. 2In [20], the authors reported the macro-averaged precision, recall, and F-score, which are incomparable to other performance results. Following the general convention in PPI research, we compared the performance using the precision, recall and F-score computed with only positive class prediction results. The original implementation was not available. We implemented it on SVM-LIGHT-TK ver 1.2 obtained from http://disi.unitn.it/moschitti/Tree-Kernel.htm. The optimization parameters used are C = 8 and λ = 0.6 (as reported in [20])