Connectionist learning procedures
Artificial Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning to recognize promoter sequences in E. coli by modeling uncertainty in the training data
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Input Reconstruction Reliability Estimation
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Dimension reduction by local principal component analysis
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Hybrid Feedforward Neural Networks for Solving Classification Problems
Neural Processing Letters
A Mixture Approach to Novelty Detection Using Training Data with Outliers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
User Profiling for Intrusion Detection Using Dynamic and Static Behavioral Models
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Kernel Whitening for One-Class Classification
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
A Recognition-Based Alternative to Discrimination-Based Multi-layer Perceptrons
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
One-class svms for document classification
The Journal of Machine Learning Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Web Intelligence and Agent Systems
Classification and knowledge discovery in protein databases
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Nonlinear Autoassociation Is Not Equivalent to PCA
Neural Computation
Neural Computation
One-class document classification via Neural Networks
Neurocomputing
The class imbalance problem: A systematic study
Intelligent Data Analysis
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Expert Systems with Applications: An International Journal
Growing a multi-class classifier with a reject option
Pattern Recognition Letters
Imbalanced text classification: A term weighting approach
Expert Systems with Applications: An International Journal
A One-Class Classification Approach for Protein Sequences and Structures
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
ACM Computing Surveys (CSUR)
Acquisition of a classification model for a risk search system from unbalanced textual examples
International Journal of Business Intelligence and Data Mining
A Comparative Study of Outlier Detection Algorithms
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A one class KNN for signal identification: a biological case study
International Journal of Knowledge Engineering and Soft Data Paradigms
Refining image retrieval using one-class classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Diversity exploration and negative correlation learning on imbalanced data sets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Handling class imbalance problem in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Feature extraction for one-class classification
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Towards one-class pattern recognition in brain activity via neural networks
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Learning without default: a study of one-class classification and the low-default portfolio problem
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Resampling methods versus cost functions for training an MLP in the class imbalance context
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
A novel method for classifying subfamilies and sub-subfamilies of g-protein coupled receptors
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
A novel synthetic minority oversampling technique for imbalanced data set learning
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feasibility of error-related potential detection as novelty detection problem in p300 mind spelling
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A self-organized neural comparator
Neural Computation
Pattern Recognition
Approximate polytope ensemble for one-class classification
Pattern Recognition
Outlier Detection by Interaction with Domain Experts
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Novelty Detection techniques are concept-learning methods that proceed by recognizing positive instances of a concept rather than differentiating between its positive and negative instances. Novelty Detection approaches consequently require very few, if any, negative training instances. This paper presents a particular Novelty Detection approach to classification that uses a Redundancy Compression and Non-Redundancy Differentiation technique based on the [Gluck & Myers, 1993] model of the hippocampus, a part of the brain critically involved in learning and memory. In particular, this approach consists of training an autoencoder to reconstruct positive input instances at the output layer and then using this autoencoder to recognize novel instances. Classification is possible, after training, because positive instances are expected to be reconstructed accurately while negative instances are not. The purpose of this paper is to compare HIPPO, the system that implements this technique, to C4.5 and feedforward neural network classification on several applications.