A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

<div>The effect of Extrapolation Borderline-SMOTE. X with no frame is the sample belonging to the minority after Extrapolation Borderline-SMOTE. A synthetic sample labeling to the minority explores towards the actual boundary and it seldom violates the original decision boundary with the help of <svg height="9.49473pt" id="M68" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -9.28833 6.84582 9.49473" width="6.84582pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M494 514C482 587 419 712 303 712C238 712 174 667 174 603C174 561 205 514 249 449C219 438 187 422 162 407C93 366 23 283 23 177C23 69 87 -12 190 -12C244 -12 288 5 328 33C406 87 444 170 444 249C444 329 404 391 331 475C265 550 222 605 222 627C222 647 238 657 267 657C355 657 421 585 484 499L494 514ZM359 234C359 143 319 30 219 30C172 30 114 75 114 178C114 275 163 343 195 378C212 397 241 415 269 425C305 382 359 313 359 234Z" id="g113-226"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="517" vert-adv-y="517"></glyph.data></g></svg>.</div>

Computational Intelligence and Neuroscience

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Figure 3

Figure 3: A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM 