A fuzzy citation-kNN algorithm for multiple instance learning

In multiple instance learning (MIL) setting, instances are grouped together in different labeled bags and the classifier tries to learn the label of unknown bags or instances. This is significantly different from traditional supervised learning techniques where the instances are labeled itself. In this work, a fuzzy based citation-kNN technique, which uses modified Hausdorff distance between bags, is introduced. Introduction of a fuzzy distance measure helps to solve the problem of overlapping bags. Effect of false positive instances in a positive bag are also reduced by calculating a fuzzy class membership for the training bags. Experiments on drug discovery and image datasets show that the performance of the proposed algorithm (MI-FCKNN) is better than the traditional citation-kNN and competitive with most state-of-the-art algorithms.

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

[2]  Joseph F. Murray,et al.  Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application , 2005, J. Mach. Learn. Res..

[3]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

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

[5]  Paul A. Viola,et al.  Multiple Instance Boosting for Object Detection , 2005, NIPS.

[6]  Anhar Risnumawan,et al.  A Scene Image is Nonmutually Exclusive—A Fuzzy Qualitative Scene Understanding , 2014, IEEE Transactions on Fuzzy Systems.

[7]  Zhi-Hua Zhou,et al.  Multi-instance learning by treating instances as non-I.I.D. samples , 2008, ICML '09.

[8]  Horst Bischof,et al.  MIForests: Multiple-Instance Learning with Randomized Trees , 2010, ECCV.

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

[10]  Jun Zhou,et al.  MILIS: Multiple Instance Learning with Instance Selection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Zhongming Zhao,et al.  MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets , 2015, Scientific Reports.

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

[13]  A GoldmanSally,et al.  Multiple instance learning of real valued data , 2003 .

[14]  Yan Zhou,et al.  A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters , 2008, J. Mach. Learn. Res..

[15]  Boris Babenko,et al.  Multiple Instance Learning with Manifold Bags , 2011, ICML.

[16]  G. A. Edgar.  Measure, Topology, and Fractal Geometry , 1990 .

[17]  Hongbin Zha,et al.  Adaptive p-posterior mixture-model kernels for multiple instance learning , 2008, ICML '08.

[18]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[19]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Horst Bischof,et al.  On-line semi-supervised multiple-instance boosting , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[23]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

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

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