Survey of Nearest Neighbor Condensing Techniques <s> V. METAHEURISTICS: IMPROVING KNN <s> This paper presents a new approach to the selection of prototypes for the nearest neighbor rule which aims at obtaining an optimal or close-to-optimal solution. The problem is stated as a constrained optimization problem using the concept of consistency. In this context, the proposed method uses tabu search in the space of all possible subsets. Comparative experiments have been carried out using both synthetic and real data in which the algorithm has demonstrated its superiority over alternative approaches. The results obtained suggest that the tabu search condensing algorithm offers a very good tradeoff between computational burden and the optimality of the prototypes selected. <s> BIB001 </s> Survey of Nearest Neighbor Condensing Techniques <s> V. METAHEURISTICS: IMPROVING KNN <s> The edited k-nearest neighbor consists of the application of the k-nearest neighbor classifier with an edited training set, in order to reduce the classification error rate. This edited training set is a subset of the complete training set in which some of the training patterns are excluded. In recent works, genetic algorithms have been successfully applied to generate edited sets. In this paper we propose three improvements of the edited k-nearest neighbor design using genetic algorithms: the use of a mean square error based objective function, the implementation of a clustered crossover, and a fast smart mutation scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machine learning benchmark repository demonstrate the improvement achieved by the joint use of these three proposals. <s> BIB002 </s> Survey of Nearest Neighbor Condensing Techniques <s> V. METAHEURISTICS: IMPROVING KNN <s> k Nearest neighbor, Bayesian methods and genetic algorithms are effective methods of machine learning. In this work a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by eliminating data that make difficult to learn. Forming new data set approach is proposed according to good data on the hand. Test process is done with five of UCI machine learning datasets. These are iris, breast cancer, glass, yeast and wine data sets. Test results are investigated in collaboration with the previous works, and the success of the study is considered. <s> BIB003 </s> Survey of Nearest Neighbor Condensing Techniques <s> V. METAHEURISTICS: IMPROVING KNN <s> Utilizing feature selection in intrusion detection can remove redundant features and improve the speed of the intrusion detection system efficiently on the basis of protecting the integrity of the original data. This paper proposes a new feature selection method that is based on KNN and Tabu search algorithm. The experiment result shows that this method can remove the redundant features, and reduce the time of feature selection. This method not only guarantees the accuracy of detection but also improves the detection speed efficiently. <s> BIB004
The basic concepts of metaheuristics can be described in the abstract, without requiring a specific problem  . These algorithms can therefore use heuristics, which in turn reflect the specificity of the problem treated, except that these heuristics are controlled by a higher level strategy. In this article we present some improvements of classical kNN algorithms through the use of some metaheuristics such as genetic algorithms and tabu search. R. Gil-Pita and X. Yao proposed BIB002 three improvements of the k-nearest neighbor using genetic algorithms: the use of an objective function based on mean square error, the implementation of a clustered crossover, and a fast smart mutation scheme. In the first proposal they make use of a novel objective function based on the MSE function. They consider the kNN as a system with C outputs, so that each output is calculated using a defined equation. Therefore, the C outputs of the kNN www.ijacsa.thesai.org system are approximations of the posterior probabilities of the data. In the second proposal the authors suggest a new crossover scheme for the GA, denominated clustered crossover (CC), in order to improve the determination of the best subset of the training set that minimizes the selected objective function by considering possible to determine the relationship between the different bits of the bit stream. In the third proposal R. Gil-Pita and X. Yao describe the application of a mutation scheme that allows selecting the best gene or group of genes to be changed, taking into account the variations of the objective function with respect to each gene for a given set. They design a fast method for evaluating the error variation when each gene is changed, and they propose a mutation strategy based on these variations of the objective function. The authors denominate this mutation scheme as fast smart mutation (FSM)  , as it allows increasing the effectiveness of the mutation stage in the genetic algorithm. Compared to the use of a classic kNN classifier, obtained results demonstrate the good accuracy of the proposed GAbased technique. The authors of "A hybrid classification method of k-nearest neighbor, Bayesian methods and genetic algorithm" BIB003 introduced a hybrid technique including KNN, genetic algorithms and Bayesian method. This approach consists of eight steps that begin with the application of the Bayesian algorithm and then the generation of new data using the genetic algorithm and the application of the K-nearest neighbor's method. And finally, several iterations of these algorithms occur orchestrated by the genetic mechanism. According to the authors, the method is useful on data sets that have a small amount of data. It generates an unlimited number of data that have similar characteristics to the original data, and improves these data according to the proposed algorithm. This method shows better classification performance with respect to classic methods such as expectation maximization algorithm  used to develop it. It is intended, according to its authors, to hardware solutions with low cost based on clustering, to noisy data classification, and to classification in the data sets with little data. To overcome the limitations of kNN, an improved version of KNN, "Genetic KNN" (GKNN), was proposed by Suguna and Thanushkodi  . A genetic algorithm is combined with the k-nearest neighbor algorithm (KNN). In the proposed method, using the genetic algorithm, k-number of samples are selected for each iteration and the accuracy of the classification is calculated as fitness. The greater accuracy is recorded each time. Thus, it is not necessary to calculate the similarities between all samples, or to consider the weight of the category. The performance of GKNN classifier was compared with traditional KNN. The experiments and results show that the GKNN not only reduces the complexity of the KNN, but it improves the classification accuracy. Ceveron and Ferri BIB001 proposed a method for the prototypes selection for the nearest neighbor rule which aims to obtain an optimal or close to optimal solution by presenting a new approach based on tabu search. The particularity of the proposed tabu search is that it uses the equations from objective genetic algorithms. In this approach all possible subsets of prototypes constitute the space of solutions. Possible moves from a particular subset consist of adding or removing each of the n initial prototypes. The attribute used to declare the movement taboo is the prototype that is added or deleted. It is worth mentioning that the results obtained with TS consistently improved the classical condensing techniques previously published in the literature using the Iris database. Wu's article BIB004 provides a method of selecting items based on the tabu search algorithm and KNN. First, the KNN algorithm is used to generate the initial solution needed for the tabu search. Then, the tabu algorithm is applied to obtain an optimal subset of items. The KNN algorithm uses the relevance of the elements to eliminate those redundant in large networks data, and the subset obtained is the initial solution of the tabu search algorithm. This algorithm was tested via creating intrusion detection model. The tests show that by using the feature selection method proposed in this paper, the detection performance of the intrusion detection system is effectively improved without compromising detection accuracy, and the detection time and accuracy performance of the system is much better than that of the current feature selection methods. As a result the method proposed in this paper has proved to be effective and feasible.