Reliable AllPairs Evolving Fuzzy Classifiers Edwin Lughofer and

Reliable All-Pairs Evolving Fuzzy Classifiers Edwin Lughofer and Oliver Buchtala IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 21, NO. 4, AUGUST 2013

Outline • CLASSIFIER STRUCTURE • TRAINING PHASE • CLASSIFICATION PHASE • Experiment

CLASSIFIER STRUCTURE

CLASSIFIER STRUCTURE •

CLASSIFIER STRUCTURE • In this paper, we are concentrating on two fuzzy classification architectures: – singleton class labels – regression-based classifiers

Singleton Class Labels •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE input (new center)

TRAINING PHASE H: Class k Cluster 1 0 Cluster 2 Cluster 3 …. …. …. . Cluster n Class L 1

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE Cluster 1 input class k class L center

TRAINING PHASE Cluster 1 input class k class L center

TRAINING PHASE Cluster 1 input class k class L center

TRAINING PHASE •

TRAINING PHASE •

CLASSIFICATION PHASE • The classification outputs are produced in two stages. – 1) The first stage produces the output confidence levels (preferences) for each class pair and stores it in the preference relation matrix – 2) The second stage uses the whole information of the preference matrix and produces a final class response.

CLASSIFICATION PHASE •

CLASSIFICATION PHASE => [0. 2 0. 2]=2. 0 [0. 8 0. 0 0. 0]=1. 6 ……… output

Regression-Based Classifiers •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

TRAINING PHASE •

CLASSIFICATION PHASE •

CLASSIFICATION PHASE ……… output

Experiment