# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
## 6 Conclusions and Future Work  
Prototype Generation methods stand as one of the possible Data Reduction strategies for improving the efficiency of the _k_-Nearest Neighbor classifier. These methods perform transformations and combinations of the prototypes in the initial set of data with the aim of reducing its size and improving, if possible, the success rate of the classifier. Despite its usefulness in the Pattern Recognition field, these methods are constrained by the representation space considered. Hence, while the aforementioned transformation and combination operations are straightforward to apply in the context of a statistical or feature-based space, their definition in the case of structural data requires some additional considerations. In this work we present the adaptation of a state-of-the-art Prototype Generation strategy to the case of string data, more precisely the Reduction through Homogeneous Clusters method. This algorithm addresses the reduction process by recursively applying a clustering process on the initial data until a set of class-homogeneous groups is retrieved for then generating a single element from each cluster as the median value of the prototypes in it. In our case this problem has been tackled with the retrieval of the set median value the string data at issue. The experimentation carried out compares the performance of the original with the proposed algorithm for the same data in both representation spaces using a Dissimilarity Space mapping process. Results obtained show that the relevance of the proposal as it avoids the representation gap of the mapping process as well as showing the best compromise between classification rate and data reduction. In light of the results obtained, a first point which should be considered is the possibility of approximating the actual median string for the prototype generation stage instead of considering the set median value. In principle, the results presented in this work should constitute a lower bound in terms of performance. Thus, more sophisticated policies for the retrieval of the median string should report better representation capabilities and, hence, higher classification rates. As noted, the retrieval of the median value is totally dependent on the distance considered. In this regard, the use of alternative string-based distance metrics as, for instance, the Cosine similarity, could report additional conclusions. Another aspect to address is the adaptation of other generation-based Data Reduction strategies to the string space. While this work proves the validity and interest of adapting a feature-based reduction  
Figure 3: Boxplot graph of the data distributions considered the statistical analysis. Data is grouped according to the representation space considered and the reduction strategy performed (note that ALL stands for the case in which no reduction is performed). All values have been scaled up two orders of magnitude for an easier understanding of the plot. method to this particular representation space, other strategies may obtain sharper reduction rates and with higher classification figures, thus being a promising path to explore. In addition, extending these developments to other structural-based codifications as, for instance, tree structures or graphs also constitutes an important research challenge due to the complexity of the generation operations in these representations,  
Finally, a last point to consider as future work is the application of these reductions string-reduction strategies as a preprocessing stage other dissimilarity-based algorithms as, for instance, Support Vector Machines or Nearest Class Mean rule, among others. [PERSON]-[PERSON] and [PERSON] equally contributed in the conception of the work, experimental, data analysis, and paper writing. All authors have read and agreed to the published version of the manuscript.