Upper and Lower Tight Error Bounds for Feature Omission with an Extension to Context Reduction

Area of Science:

• Machine Learning
• Pattern Recognition
• Computational Linguistics

Background:

• Feature selection and context reduction are critical in pattern and string classification.
• Quantifying their impact on classification error is essential for model optimization.
• Existing methods lack comprehensive analytical bounds for these processes.

Purpose of the Study:

• To derive fundamental analytic error bounds for feature omission, selection, and context reduction.
• To establish a simulation framework for discovering and proving these error bounds.
• To identify a statistical threshold for guaranteed error reduction in classification.

Main Methods:

• Development of a general simulation framework for error bound analysis.
• Derivation of tight lower and upper bounds for feature omission and selection.
• Extension of bounds to context reduction in string classification (language models).

Main Results:

• Quantified the effect of feature omission and selection on general pattern classification error.
• Presented bounds for context reduction in string classification, impacting Bayes error.
• Demonstrated that combining feature omission and context reduction retains bound tightness.
• Proved the existence and amount of a statistical threshold for guaranteed error decrease.

Conclusions:

• Analytic error bounds provide crucial insights into feature selection and context reduction.
• A statistically significant threshold guarantees improved classification performance.
• The findings are applicable to diverse fields like speech recognition and machine translation.