KNN.h File Reference

This class implements the K-Nearest Neighbor classification algorithm (http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm). KNN is a simple but powerful classifier, based on finding the closest K training examples in the feature space for the new input vector. The KNN algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. More...

#include "../../CoreModules/Classifier.h"

Go to the source code of this file.

Classes
class	KNN

Macros
#define	BIG_DISTANCE   99e+99

Detailed Description

This class implements the K-Nearest Neighbor classification algorithm (http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm). KNN is a simple but powerful classifier, based on finding the closest K training examples in the feature space for the new input vector. The KNN algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor.

Author

Nicholas Gillian ngill.nosp@m.ian@.nosp@m.media.nosp@m..mit.nosp@m..edu

Version

1.0

This implementation of the algorithm will return the class label of the class that gains the majoriy vote of its neighbours. If the average distance of the closest K neighbors with the class label of the majority vote is greater than that of that classes rejection threshold, then the predicted class label will be set to 0, indicating that the majority class was rejected. This feature can be enabled or disabled by setting the enableNullRejection paramter to false.

Definition in file KNN.h.

Macro Definition Documentation

#define BIG_DISTANCE   99e+99

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 49 of file KNN.h.