Decay-weighted extreme learning machine for balance and optimization learning

The original extreme learning machine (ELM) was designed for the balanced data, and it balanced misclassification cost of every sample to get the solution. Weighted extreme learning machine assumed that the balance can be achieved through the equality of misclassification costs. This paper improves previous weighted ELM with decay-weight matrix setting for balance and optimization learning. The decay-weight matrix is based on the sample number of each class, but the weight sum values of each class are not necessarily equal. When the number of samples is reduced, the weight sum is also reduced. By adjusting the decaying velocity, classifier could achieve more appropriate boundary position. From the experimental results, the decay-weighted ELM obtains the better effects in solving the imbalance classification tasks, particularly in multiclass tasks. This method was successfully applied to build the prediction model in the urban traffic congestion prediction system.

[1]  Chunyan Miao,et al.  Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings , 2014, Neurocomputing.

[2]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Jia Xu,et al.  Extreme learning machines: new trends and applications , 2014, Science China Information Sciences.

[4]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[5]  Zhi-Hua Zhou,et al.  Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Yiqiang Chen,et al.  Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.

[7]  Beata Strack,et al.  Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records , 2014, BioMed research international.

[8]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[9]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[10]  Qinghua Zheng,et al.  Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[11]  Lee,et al.  Weighted Learning for Feedforward Neural Networks , 2014 .

[12]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Salvatore J. Stolfo,et al.  Cost-based modeling for fraud and intrusion detection: results from the JAM project , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[14]  Wei Wang,et al.  Improved Convex Incremental Extreme Learning Machine Based on Enhanced Random Search , 2014 .

[15]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[16]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[17]  Cheng Wu,et al.  Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.

[18]  Ying Jing Ke Hai-feng.  Real-time license character recognition technology based on R-ELM , 2014 .

[19]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[20]  T. M. Williams,et al.  Practical Methods of Optimization. Vol. 1: Unconstrained Optimization , 1980 .

[21]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[22]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[23]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[24]  Hervé Glotin,et al.  Optimizing widths with PSO for center selection of Gaussian radial basis function networks , 2013, Science China Information Sciences.