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This study introduces a novel multiple-instance learning (MIL) formulation using infinitely many shapelets for robust bag classification. The proposed method, reducible to difference of convex programs, offers theoretical justification and practical efficiency for time-series and MIL tasks.

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Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Mining

Background:

  • Multiple-instance learning (MIL) classifies data where each instance is a bag of points.
  • Existing MIL methods often select shapelets (patterns) heuristically.
  • A need exists for a theoretically grounded and efficient MIL framework.

Purpose of the Study:

  • To propose a new MIL formulation utilizing all possible shapelets.
  • To demonstrate the tractability and theoretical justification of the proposed method.
  • To develop an efficient algorithm for large-scale MIL tasks.

Main Methods:

  • A novel formulation of multiple-instance learning (MIL) using an infinite set of shapelets.
  • Reduction of the MIL problem to difference of convex (DC) programs via linear programming boosting (LPBoost).
  • Development of a heuristic algorithm for computational efficiency on large datasets.

Main Results:

  • The proposed formulation allows for a richer class of classifiers with theoretical guarantees.
  • The method is shown to be tractable and reducible to finite-sized DC programs.
  • Empirical studies confirm comparable accuracy to existing methods on time-series classification and MIL tasks.
  • Heuristic options achieve results in reasonable computational time.

Conclusions:

  • The novel MIL formulation provides a theoretically sound and practically applicable approach to shapelet learning.
  • The method offers a justified alternative to previous heuristic shapelet selection.
  • The algorithm demonstrates effectiveness and efficiency for various classification tasks.