A Hybrid KNN-Ant Colony Optimization Algorithm for Prototype Selection

Miloud-Aouidate, Amal; Baba-Ali, Ahmed Riadh

doi:10.1007/978-3-642-34487-9_38

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Cluster-oriented instance selection (CIS) 21 runned unsupervised K-Means Clustering (KMC) algorithm to identify clusters in the training instances and using distance based measures to select instances from the centers and the borders of the clusters. The evolutionary prototype methods,such as ANN-KNN 22 , and DWP 23 , etc. are introduced to improve the performance of the condensing KNN algorithm or NN classifier, handle the class imbalance problem, etc.…”

Section: Related Workmentioning

confidence: 99%

Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation

Cai

2022

Sci Rep

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The unceasing increase of data quantity severely limits the wide application of mature classification algorithms due to the unacceptable execution time and the insufficient memory. How to fast incrementally obtain high decision reference set and adapt to incremental data environment is urgently needed in incremental environments, large dataset, etc. This paper proposes a novel prototype selection algorithm by integrating the strategies between condensing method and editing method. To an unlearned pattern, this algorithm extends the references scope from its single nearest neighbour to its k nearest neighbourhood that can expand the judgment information to obtain its detailed neighbour relationship. Then a pattern was determined whether it is a prototype using its neighbour relationship and classification boundary asymptotically strategy. To maintain the higher reference set, this algorithm periodically updates those prototypes that locates in the non-boundary zone or is long-time unlearned. The empirical study shows that this algorithm obtains the smaller and higher boundary prototypes without decreasing classification accuracy and reduction rate than the compared algorithms.

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Section: Related Workmentioning

confidence: 99%

Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation

Cai

2022

Sci Rep

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A novel technique: ensemble hybrid 1NN model using stacking approach

Nair

Khatri

Kashyap

2018

Int. j. inf. tecnol.

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An Efficient Ant Colony Instance Selection Algorithm for KNN Classification

Miloud-Aouidate

Baba-Ali

2013

International Journal of Applied Metaheuristic Computing

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The extraordinary progress in the computer sciences field has made Nearest Neighbor techniques, once considered impractical from a standpoint of computation (Dasarathy et al., 2003), became feasible for real-world applications. In order to build an efficient nearest neighbor classifier two principal objectives have to be reached: 1) achieve a high accuracy rate; and 2) minimize the set of instances to make the classifier scalable even with large datasets. These objectives are not independent. This work addresses the issue of minimizing the computational resource requirements of the KNN technique, while preserving high classification accuracy. This paper investigates a new Instance Selection method based on Ant Colonies Optimization principles, called Ant Instance Selection (Ant-IS) algorithm. The authors have proposed in a previous work (Miloud-Aouidate & Baba-Ali, 2012a) to use Ant Colony Optimization for preprocessing data for Instance Selection. However to the best of the authors’ knowledge, Ant Metaheuristic has not been used in the past for directly addressing Instance Selection problem. The results of the conducted experiments on several well known data sets are presented and compared to those obtained using a number of well known algorithms, and most known classification techniques. The results provide evidence that: (1) Ant-IS is competitive with the well-known kNN algorithms; (2) The condensed sets computed by Ant-IS offers also better classification accuracy then those obtained by the compared algorithms.

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A Hybrid KNN-Ant Colony Optimization Algorithm for Prototype Selection

Cited by 3 publications

References 10 publications

Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation

Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation

A novel technique: ensemble hybrid 1NN model using stacking approach

An Efficient Ant Colony Instance Selection Algorithm for KNN Classification

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