While standing as one of the most widely considered and successful supervised classification algorithms, the k-Nearest Neighbor (kNN) classifier generally depicts a poor efficiency due to being an instance-based method. In this sense, Approximated Similarity Search (ASS) stands as a possible alternative to improve those efficiency issues at the expense of typically lowering the performance of the classifier. In this paper we take as initial point an ASS strategy based on clustering. We then improve its performance by solving issues related to instances located close to the cluster boundaries by enlarging their size and considering the use of Deep Neural Networks for learning a suitable representation for the classification task at issue. Results using a collection of eight different datasets show that the combined use of these two strategies entails a significant improvement in the accuracy performance, with a considerable reduction in the number of distances needed to classify a sample in comparison to the basic kNN rule.instance at issue is not part of the cluster being examined, we include it inside the cluster, thus approaching the space partitioning to something similar to a fuzzy clustering; (iv) this process is done for each of the clusters obtained.This strategy increases the likelihood of making all the k-nearest neighbors of a given test instance fall in the same cluster. Also note that both the clustering 50 process and the proposed enlargement are done as a preprocessing stage, thus not affecting the efficiency of the classification process. As it shall be later experimentally checked, this process of increasing the cluster size approaches the brute-force kNN scenario in terms of accuracy with far less computational cost. 55Furthermore, recent advances in feature learning, namely deep learning, have made a breakthrough in the ability to learn suitable features for classification.That is, instead of resorting to hand-crafted features extracted, the models are trained to infer out of the raw input signal the most suitable features for the task at hand. This representational learning is performed by means of Deep 60 Neural Networks (DNN), consisting of a number of layers which are able to

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lowers the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.

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Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that aim at obtaining a structured digital representation from sheet music images and acoustic recordings, respectively. While these fields have traditionally evolved independently, the fact that both tasks may share the same output representation poses the question of whether they could be combined in a synergistic manner to exploit the individual transcription advantages depicted by each modality. To evaluate this hypothesis, this paper presents a multimodal framework that combines the predictions from two neural end-to-end OMR and AMT systems by considering a local alignment approach. We assess several experimental scenarios with monophonic music pieces to evaluate our approach under different conditions of the individual transcription systems. In general, the multimodal framework clearly outperforms the single recognition modalities, attaining a relative improvement close to $$40\%$$ 40 % in the best case. Our initial premise is, therefore, validated, thus opening avenues for further research in multimodal OMR-AMT transcription.

The k-nearest neighbour rule is commonly considered for classification tasks given its straightforward implementation and good performance in many applications. However, its efficiency represents an obstacle in real-case scenarios because the classification requires computing a distance to every single prototype of the training set. Prototype Selection (PS) is a typical approach to alleviate this problem, which focuses on reducing the size of the training set by selecting the most interesting prototypes. In this context, rank methods have been postulated as a good solution: following some heuristics, these methods perform an ordering of the prototypes according to their relevance in the classification task, which is then used to select the most relevant ones. This work presents a significant improvement of existing rank methods by proposing two extensions: i) a greater robustness against noise at label level by considering the parameter 'k' of the classification in the selection process; and ii) a new parameter-free rule to select the prototypes once they have been ordered. The experiments performed in different scenarios and datasets demonstrate the goodness of these extensions. Also, it is empirically proved that the new full approach is competitive with respect to existing PS algorithms.

In the current Information Age, data production and processing demands are ever increasing. This has motivated the appearance of large-scale distributed information. This phenomenon also applies to Pattern Recognition so that classic and common algorithms, such as the k-Nearest Neighbour, are unable to be used. To improve the efficiency of this classifier, Prototype Selection (PS) strategies can be used. Nevertheless, current PS algorithms were not designed to deal with distributed data, and their performance is therefore unknown under these conditions. This work is devoted to carrying out an experimental study on a simulated framework in which PS strategies can be compared under classical conditions as well as those expected in distributed scenarios. Our results report a general behaviour that is degraded as conditions approach to more realistic scenarios. However, our experiments also show that some methods are able to achieve a fairly similar performance to that of the non-distributed scenario.Thus, although there is a clear need for developing specific PS methodologies and algorithms for tackling these situations, those that reported a higher robustness against such conditions may be good candidates from which to start.