A Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models

by   Pawel Trajdos, et al.

In this work, we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting label pairwise ensembles. The main step of the correction procedure is to compute classifier- specific competence and cross-competence measures, which estimates error pattern of the underlying classifier. We considered two improvements of the method of obtaining confusion matrices. The first one is aimed to deal with imbalanced labels. The other utilizes double labelled instances which are usually removed during the pairwise transformation. The proposed methods were evaluated using 29 benchmark datasets. In order to assess the efficiency of the introduced models, they were compared against 1 state-of-the-art approach and the correction scheme based on the original method of confusion matrix estimation. The comparison was performed using four different multi-label evaluation measures: macro and micro-averaged F1 loss, zero-one loss and Hamming loss. Additionally, we investigated relations between classification quality, which is expressed in terms of different quality criteria, and characteristics of multi-label datasets such as average imbalance ratio or label density. The experimental study reveals that the correction approaches significantly outperforms the reference method only in terms of zero-one loss.


page 1

page 2

page 3

page 4


Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix

In this work we addressed the issue of applying a stochastic classifier ...

Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification

The aim of this paper was to compare soft confusion matrix approach and ...

Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network

With noisy environment caused by fluoresence and additive white noise as...

Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?

We study the performance of data-driven, a priori and random approaches ...

Comprehensive Comparative Study of Multi-Label Classification Methods

Multi-label classification (MLC) has recently received increasing intere...

ZLPR: A Novel Loss for Multi-label Classification

In the era of deep learning, loss functions determine the range of tasks...

Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting

Imbalanced dataset is occurred due to uneven distribution of data availa...

Please sign up or login with your details

Forgot password? Click here to reset