Local Rejection Strategies for Learning Vector Quantization
نویسندگان
چکیده
Classification with rejection is well understood for classifiers which provide explicit class probabilities. The situation is more complicated for popular deterministic classifiers such as learning vector quantisation schemes: albeit reject options using simple distance-based geometric measures were proposed [4], their local scaling behaviour is unclear for complex problems. Here, we propose a local threshold selection strategy which automatically adjusts suitable threshold values for reject options in prototype-based classifiers from given data. We compare this local threshold strategy to a global choice on artificial and benchmark data sets; we show that local thresholds enhance the classification results in comparison to global ones, and they better approximate optimal Bayesian rejection in cases where the latter is available.
منابع مشابه
Rejection strategies for learning vector quantization
We present prototype-based classification schemes, e. g. learning vector quantization, with cost-function-based and geometrically motivated reject options. We evaluate the reject schemes in experiments on artificial and benchmark data sets. We demonstrate that reject options improve the accuracy of the models in most cases, and that the performance of the proposed schemes is comparable to the o...
متن کاملEfficient rejection strategies for prototype-based classification
Due to intuitive training algorithms and model representation, prototypebased models are popular in settings where on-line learning and model interpretability play a major role. In such cases, a crucial property of a classifier is not only which class to predict, but also if a reliable decision is possible in the first place, or whether it is better to reject a decision. While strong theoretica...
متن کاملRejection Strategies for Learning Vector Quantization - A Comparison of Probabilistic and Deterministic Approaches
In this contribution, we focus on reject options for prototypebased classifiers, and we present a comparison of reject options based on statistical models for prototype-based classification as compared to alternatives which are motivated by simple geometric principles. We compare the behavior of generative models such as Gaussian mixture models and discriminative ones to results from robust sof...
متن کاملA class-modular GLVQ ensemble with outlier learning for handwritten digit recognition
A class-modular generalized learning vector quantization (GLVQ) ensemble method with outlier learning for handwritten digit recognition is proposed. A GLVQ classifier is one of discriminative methods. Though discriminative classifiers have remarkable ability to solve character recognition problems, they are poor at outlier resistance. To overcome this problem, a GLVQ classifier trained with bot...
متن کاملNGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...
متن کامل