Machine Learning Techniques for Solving Classification Problems with Missing Input Data

Missing input data is a common drawback in many real-life pattern classification scenarios. The ability of missing data handling has become a fundamental requirement for pattern classification because an inappropriate treatment may cause large errors or false results on classification. The absence of certain values for relevant data attributes can seriously affect the accuracy of classification results. The goal of this paper is to analyze the missing data problem in pattern classification, and to make a descriptive survey of machine learning solutions for performing incomplete pattern classification tasks.

[1]  Robi Polikar,et al.  An ensemble of classifiers approach for the missing feature problem , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[2]  Leonardo Franco,et al.  Missing data imputation in breast cancer prognosis , 2006 .

[3]  Hisao Ishibuchi,et al.  Classification of fuzzy input patterns by neural networks , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Peter K. Sharpe,et al.  Dealing with missing values in neural network-based diagnostic systems , 1995, Neural Computing & Applications.

[5]  Geoffrey I. Webb.  The Problem of Missing Values in Decision Tree Grafting , 1998, Australian Joint Conference on Artificial Intelligence.

[6]  Jinbo Bi,et al.  Support Vector Classification with Input Data Uncertainty , 2004, NIPS.

[7]  T. Marwala,et al.  Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm , 2006 .

[8]  Bogdan Gabrys,et al.  Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems , 2002, Int. J. Approx. Reason..

[9]  S. Nordbotten.  Neural network imputation applied to the Norwegian 1990 population census data , 1996 .

[10]  Volker Tresp,et al.  Training Neural Networks with Deficient Data , 1993, NIPS.

[11]  Michael R. Berthold,et al.  Missing Values and Learning of Fuzzy Rules , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[12]  Robert P. W. Duin,et al.  Combining One-Class Classifiers to Classify Missing Data , 2004, Multiple Classifier Systems.

[13]  Aníbal R. Figueiras-Vidal,et al.  Pattern Classification with Missing Values using Multitask Learning , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[14]  Volker Tresp,et al.  Efficient Methods for Dealing with Missing Data in Supervised Learning , 1994, NIPS.

[15]  Gustavo E. A. P. A. Batista,et al.  A Study of K-Nearest Neighbour as an Imputation Method , 2002, HIS.

[16]  G. DiCesare.  Imputation, Estimation and Missing Data in Finance , 2006 .

[17]  Aníbal R. Figueiras-Vidal,et al.  Multi-task Neural Networks for Dealing with Missing Inputs , 2007, IWINAC.

[18]  Joseph L Schafer,et al.  Analysis of Incomplete Multivariate Data , 1997 .

[19]  Tariq Samad,et al.  Imputation of Missing Data in Industrial Databases , 1999, Applied Intelligence.

[20]  M. Marseguerra,et al.  The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component , 2005 .

[21]  Volker Tresp,et al.  Some Solutions to the Missing Feature Problem in Vision , 1992, NIPS.

[22]  Alexander J. Smola,et al.  A Second Order Cone programming Formulation for Classifying Missing Data , 2004, NIPS.

[23]  Le Gruenwald,et al.  Estimating Missing Values in Related Sensor Data Streams , 2005, COMAD.

[24]  Tariq Samad,et al.  Self–organization with partial data , 1992 .

[25]  Johan A. K. Suykens,et al.  Handling missing values in support vector machine classifiers , 2005, Neural Networks.

[26]  Yoshua Bengio,et al.  Recurrent Neural Networks for Missing or Asynchronous Data , 1995, NIPS.

[27]  Tshilidzi Marwala,et al.  Missing data: A comparison of neural network and expectation maximization techniques , 2007 .

[28]  William T. Scherer,et al.  IMPUTATION TECHNIQUES TO ACCOUNT FOR MISSING DATA IN SUPPORT OF INTELLIGENT TRANSPORTATION SYSTEMS APPLICATIONS , 2003 .

[29]  Sophie Midenet,et al.  Self-Organising Map for Data Imputation and Correction in Surveys , 2002, Neural Computing & Applications.

[30]  Lawrence Carin,et al.  On Classification with Incomplete Data , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Michael I. Jordan,et al.  Learning from Incomplete Data , 1994 .

[32]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[33]  Soo-Young Lee,et al.  Training Algorithm with Incomplete Data for Feed-Forward Neural Networks , 1999, Neural Processing Letters.

[34]  Mia K. Markey,et al.  Impact of missing data in training artificial neural networks for computer-aided diagnosis , 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..