The nearest neighbor rule identifies the category of an unknown element according to its known nearest neighbors' categories. This technique is efficient in many fields as event recognition, text categorization and object recognition. Its prime advantage is its simplicity, but its main inconvenience is its computing complexity for large training sets. This drawback was dealt by the researchers' community as the problem of prototype selection. Trying to solve this problem several techniques presented as condensing techniques were proposed. Condensing algorithms try to determine a significantly reduced set of prototypes keeping the performance of the 1-NN rule on this set close to the one reached on the complete training set. In this paper we present a survey of some condensing KNN techniques which are CNN, RNN, FCNN, Drop1-5, DEL, IKNN, TRKNN and CBP. All these techniques can improve the efficiency in computation time. But these algorithms fail to prove the minimality of their resulting set. For this, one possibility is to hybridize them with other algorithms, called modern heuristics or metaheuristics, which, themselves, can improve the solution. The metaheuristics that have proven results in the selection of attributes are principally genetic algorithms and tabu search. We will also shed light in this paper on some recent techniques focusing on this template.

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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.

During recent years, the number of attacks on networks has dramatically increased. Consequently the interest in network intrusion detection has increased among the researchers. This paper proposes a clustering Ant-IS and an active Ant colony optimization algorithms for intrusion detection in computer networks. The goal of these algorithms is to extract a set of learning instances from the initial training dataset. The proposed algorithms are an improvement of the previously presented Ant-IS algorithm, used is pattern recognition. Results of experimental tests show that the proposed algorithms are capable of producing a reliable intrusion detection system.