Neural Networks for Multi-Instance Learning

Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its unique characteristics and extensive existence, multi-instance learning is regarded as a new machine learning framework parallel to supervised learning, unsupervised learning, and reinforcement learning. In this paper, an open problem of this area is addressed. That is, a popular neural network algorithm is adapted for multi-instance learning through employing a specific error function. Experiments show that the adapted algorithm achieves good result on the drug activity prediction data.

[1]  Joshua Lederberg,et al.  Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project , 1980 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  Michael Kearns,et al.  Efficient noise-tolerant learning from statistical queries , 1993, STOC.

[5]  Robert E. Schapire,et al.  Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[6]  Philip M. Long,et al.  PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples , 1996, COLT.

[7]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[8]  Jean-Gabriel Ganascia,et al.  Representation Changes for Efficient Learning in Structural Domains , 1996, ICML.

[9]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[10]  Aravind Srinivasan,et al.  Approximating hyper-rectangles: learning and pseudo-random sets , 1997, STOC '97.

[11]  Michèle Sebag,et al.  Tractable Induction and Classification in First Order Logic Via Stochastic Matching , 1997, IJCAI.

[12]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[13]  Peter Auer,et al.  On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach , 1997, ICML.

[14]  Luc De Raedt,et al.  Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract) , 1998, ILP.

[15]  Oded Maron,et al.  Learning from Ambiguity , 1998 .

[16]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[17]  Jean-Gabriel Ganascia,et al.  Learning Structurally Indeterminate Clauses , 1998, ILP.

[18]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[19]  Tomás Lozano-Pérez,et al.  Image database retrieval with multiple-instance learning techniques , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[20]  Yann Chevaleyre,et al.  Solving multiple-instance and multiple-part learning problems with decision trees and decision rules . Application to the mutagenesis problem , 2000 .

[21]  Giancarlo Ruffo,et al.  Learning single and multiple instance decision tree for computer security applications , 2000 .

[22]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[23]  Yann Chevaleyre,et al.  A Framework for Learning Rules from Multiple Instance Data , 2001, ECML.

[24]  Sally A. Goldman,et al.  Multiple-Instance Learning of Real-Valued Data , 2001, J. Mach. Learn. Res..

[25]  Stephen Kwek,et al.  Agnostic Learning of Geometric Patterns , 2001, J. Comput. Syst. Sci..

[26]  David Page,et al.  Multiple Instance Regression , 2001, ICML.

[27]  Yann Chevaleyre,et al.  Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem , 2001, Canadian Conference on AI.

[28]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[29]  Sally A. Goldman,et al.  Multiple-Instance Learning of Real-Valued Geometric Patterns , 2003, Annals of Mathematics and Artificial Intelligence.

[30]  Adam Tauman Kalai,et al.  A Note on Learning from Multiple-Instance Examples , 2004, Machine Learning.