Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification
Jorge Calvo-Zaragoza 1 - Personal Name
Jose J. Valero-Mas 1 - Personal Name
Juan R. Rico-Juan 1 - Personal Name
The nearest neighbor rule is one of the most con- sidered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Pro- totype Reduction algorithms that are capable of generating
a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original
training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accu- racy can be obtained compared to using the original training set, with a significantly higher efficiency.
a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original
training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accu- racy can be obtained compared to using the original training set, with a significantly higher efficiency.
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