Prediction of Defective Software Modules Using Class Imbalance Learning

<div>The influence of penalty parameters on <svg height="8.8423pt" id="M95" style="vertical-align:-0.2064009pt" version="1.1" viewbox="-0.0498162 -8.6359 9.02496 8.8423" width="9.02496pt" 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="M673 297H417L411 270C517 261 522 258 506 183L487 92C476 38 428 19 360 19C207 19 123 132 123 287C123 472 243 631 441 631C557 631 617 583 617 469L647 472C650 545 655 604 658 632C625 643 554 666 467 666C201 666 23 502 23 278C23 90 156 -16 339 -16C410 -16 501 6 569 24C566 43 567 70 573 101L589 183C604 259 607 262 667 271L673 297Z" id="g113-72"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="684" vert-adv-y="684"></glyph.data></g></svg>-Mean of WLSTSVM.</div>

Applied Computational Intelligence and Soft Computing

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Figure 2

Figure 2: Prediction of Defective Software Modules Using Class Imbalance Learning 