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Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability.

Shotaro Watanabe1, Hidetoshi Matsui2

  • 1Graduate School of Data Science, Shiga University, Shiga 522-8522, Japan s6021142@st.shiga-u.ac.jp.

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Summary

This study introduces a novel method for positive and biased negative (PbN) classification, addressing challenges with skewed data. The approach corrects for biased observations, improving classifier accuracy in weakly supervised learning scenarios.

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Area of Science:

  • Machine Learning
  • Computer Science

Background:

  • Binary classification tasks often encounter imbalanced datasets where one class has significantly fewer observed instances.
  • Existing methods struggle with biased observations, leading to inaccurate classifiers.

Purpose of the Study:

  • To propose a new method for the positive and biased negative (PbN) classification problem.
  • To develop a weakly supervised learning approach that handles skewed data effectively.

Main Methods:

  • Introduced a novel method to correct the negative influence of skewed confidence in biased negative data.
  • Incorporated a technique to adjust the posterior probability of observed data being positive.
  • Reduced distortion in the posterior probability of data labeling for empirical risk minimization.

Main Results:

  • Demonstrated the effectiveness of the proposed method through experiments with synthetic and benchmark datasets.
  • Showcased improved classifier performance in the presence of biased negative data.

Conclusions:

  • The proposed method offers a robust solution for PbN classification problems.
  • Effective handling of biased data is crucial for accurate weakly supervised learning.