Neural networks and the bias/variance dilemma
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Optimizing classifiers for imbalanced training sets
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Incremental class learning approach and its application to handwritten digit recognition
Information Sciences—Informatics and Computer Science: An International Journal
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Are loss functions all the same?
Neural Computation
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Neural Computation
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Multi-category classification by least squares support vector regression
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
On loss functions which minimize to conditional expected values and posterior probabilities
IEEE Transactions on Information Theory
Cost functions to estimate a posteriori probabilities in multiclass problems
IEEE Transactions on Neural Networks
Building cost functions minimizing to some summary statistics
IEEE Transactions on Neural Networks
5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Information Sciences: an International Journal
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Online learning neural tracker
Neurocomputing
Information Sciences: an International Journal
Information Sciences: an International Journal
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Expert Systems with Applications: An International Journal
Complex-Valued neuro-fuzzy inference system based classifier
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Hi-index | 0.07 |
In this paper, we propose two risk-sensitive loss functions to solve the multi-category classification problems where the number of training samples is small and/or there is a high imbalance in the number of samples per class. Such problems are common in the bio-informatics/medical diagnosis areas. The most commonly used loss functions in the literature do not perform well in these problems as they minimize only the approximation error and neglect the estimation error due to imbalance in the training set. The proposed risk-sensitive loss functions minimize both the approximation and estimation error. We present an error analysis for the risk-sensitive loss functions along with other well known loss functions. Using a neural architecture, classifiers incorporating these risk-sensitive loss functions have been developed and their performance evaluated for two real world multi-class classification problems, viz., a satellite image classification problem and a micro-array gene expression based cancer classification problem. To study the effectiveness of the proposed loss functions, we have deliberately imbalanced the training samples in the satellite image problem and compared the performance of our neural classifiers with those developed using other well-known loss functions. The results indicate the superior performance of the neural classifier using the proposed loss functions both in terms of the overall and per class classification accuracy. Performance comparisons have also been carried out on a number of benchmark problems where the data is normal i.e., not sparse or imbalanced. Results indicate similar or better performance of the proposed loss functions compared to the well-known loss functions.