You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey on pre-processing techniques: Relevant issues in the context of environmental data mining 
Chapter Title:  Filter instance selection methods 
1. Abstract of Cited Paper (BIB001):  Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly. 
2. Abstract of Cited Paper (BIB002):  It is well known that editing techniques can be applied to (large) sets of prototypes in order to bring the error rate of the nearest neighbour classifier close to the optimal Bayes risk. However, in practice, the behaviour of these techniques is often much worse than expected from the asymptotic predictions. A novel editing technique is introduced, which explicitly aims at obtaining a good editing rule for each given prototype set. This is achieved by first learning an adequate assignment of a weight to each prototype and then pruning those prototypes having large weights. Experiments are presented which clearly show the superiority of this new method, specially for small data sets and/or large dimensions. 
3. Abstract of Cited Paper (BIB003):  Abstract A generalized prototype-based classification scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the two same-class nearest clusters, provided that the set of cluster representatives correctly classifies all the original points. Apart from the quality of the obtained sets and its flexibility which comes from the fact that different intercluster measures and criteria can be used, the proposed scheme includes a very efficient four-stage procedure which conveniently exploits geometric cluster properties to decide about each possible merge. Empirical results demonstrate the merits of the proposed algorithm taking into account the size of the condensed sets of prototypes, the accuracy of the corresponding condensed 1-NN classification rule and the computing time. 
4. Abstract of Cited Paper (BIB004):  This paper presents a new approach to 2nding representative patterns for dataset editing. The algorithm patterns by ordered projections (POP), has some interesting characteristics: important reduction of the number of instances from the dataset; lower computational cost (� (mn log n)) with respect to other typical algorithms due to the absence of distance calculations; conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classi2ers. POP works well in practice withbothcontinuous and discrete attributes. The performance of POP is analysed in two ways: percentage of reduction and classi2cation. POP has been compared to IB2, ENN and SHRINK concerning the percentage of reduction and the computational cost. In addition, we have analysed the accuracy of k-NN and C4.5 after applying the reduction techniques. An extensive empirical study using datasets with continuous and discrete attributes from the UCI repository shows that POP is a valuable preprocessing method for the later application of any axis-parallel learning algorithm. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. 
5. Abstract of Cited Paper (BIB005):  We present the nearest subclass classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the maximum variance cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency. 
6. Abstract of Cited Paper (BIB006):  This paper presents a new method based on divide-and-conquer approach to the selection and replacement of a set of prototypes from the training set for the nearest neighbor rule. This method aims at reducing the computational time and the memory space as well as the sensitivity of the order and the noise of the training data. A reduced prototype set contains Pairwise Opposite Class-Nearest Neighbor (POC-NN) prototypes which are close to the decision boundary and used instead of the training patterns. POC-NN prototypes are obtained by recursively iterative separation and analysis of the training data into two regions until each region is correctly grouped and classified. The separability is determined by the POC-NN prototypes essential to define the locations of all separating hyperplanes. Our method is fast and order independent. The number of prototypes and the overfitting of the model can be reduced by the user. The experimental results signify the effectiveness of this technique and its performance in both accuracy and prototype rate as well as in training time to those obtained by classical nearest neighbor techniques. 
7. Abstract of Cited Paper (BIB007):  A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classifiers known from statistical pattern recognition, this is only a very basic method. In the present paper we propose a method for transforming strings into n-dimensional real vector spaces based on prototype selection. This allows us to subsequently classify the transformed strings with more sophisticated classifiers, such as support vector machine and other kernel based methods. In a number of experiments, we show that the recognition rate can be significantly improved by means of this procedure. 
8. Abstract of Cited Paper (BIB008):  Object selection is an important task for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented. 
9. Abstract of Cited Paper (BIB009):  In Pattern recognition, the supervised classifiers use a training set Tfor classifying new prototypes. In practice, not all information in Tis useful for classification therefore it is necessary to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor (NN) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods. 
10. Abstract of Cited Paper (BIB010):  Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such data streams evolve over time, that is when concepts drift or change completely, is becoming one of the core issues. When tackling non-stationary concepts, ensembles of classifiers have several advantages over single classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept descriptions. This paper proposes a new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. Using the new experimental framework, an evaluation study on synthetic and real-world datasets comprising up to ten million examples shows that the new ensemble methods perform very well compared to several known methods. 
11. Abstract of Cited Paper (BIB011):  Instance selection is a feasible strategy to solve the problem of dealing with large databases in inductive learning. There are several proposals in this area, but none of them consistently outperforms the others over a wide range of domains. In this paper we present a set of measures to characterize the databases, as well as a new algorithm that uses these measures and, depending on the data characteristics, it applies the method or combination of methods expected to produce the best results. This approach was evaluated over 20 databases and with six different learning paradigms. The results have been compared with those achieved by five well-known state-of-the-art methods. 
12. Abstract of Cited Paper (BIB012):  Thank you very much for downloading resampling methods a practical guide to data analysis. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this resampling methods a practical guide to data analysis, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their desktop computer.

Literature Review:
These methods use several selection criteria, but none is based on the feedback of a mined model from the data (classifier, clustering, etc.). There are some special filtering methods, sometimes noted as exhaustive methods, which mainly use the size or some percentage of instances to make the selection process: • Random methods: take randomly just the percentage of instances that the system is able to process (sampling). • Supervised random methods: take randomly the same percentage of instances within each class group (stratified sampling). Random filters are repeatedly used as the first step of iterations in resampling methods BIB013 , like Bootstrap, Jackknife or Monte Carlo methods  and ensemble methods BIB011 BIB002 . Resampling methods are nowadays becoming very popular in the context of big data, since they belong to a divide and conquer approach that makes dealing with huge datasets possible. Also, resampling techniques, and in consequence, repeated random filters can be involved in some validation procedures as cross-validation BIB001 . Among the filter methods in the literature, we can find methods based on k-d Trees  , where a binary tree is constructed. Some authors BIB012 BIB008 have proposed the idea of using clustering for the selection process, taking the centers (or nearby) of the clusters as the selected instances. Some methods following those ideas are the GCM (Generalized-Modified Chang Algorithm) method BIB004 , the NSB (Nearest Sub-class Classifier) method BIB006 , the CLU (Clustering) instance selection method  , and the OSC (Object Selection by Clustering) BIB009 . Other authors suggested the assignment of weights to the instances and selecting only the relevant instances like the WP (Weighting Prototypes) method BIB003 and the PSR (Prototype Selection by Relevance) method BIB010 . Other filter methods are based on other semantic criteria, based on some properties about the instances. Instances are defined to be border instances or inner instances. A border instance for a class C is the nearest neighbor of an instance from another class C . Some filter methods based on selecting border instances are the POP (Pattern by Ordered Projections) method BIB005 , which discards inner instances and selects some border instances, and POC-NN (Pair Opposite Class-NearestNeighbor) method BIB007 , which selects border instances.