Search results for "HB"
showing 10 items of 1066 documents
Editing prototypes in the finite sample size case using alternative neighborhoods
1998
The recently introduced concept of Nearest Centroid Neighborhood is applied to discard outliers and prototypes 111 class overlapping regions in order to improve the performance of the Nearest Neighbor rule through an editing procedure, This approach is related to graph based editing algorithms which also define alternative neighborhoods in terms of geornetric relations, Classical editing algorithms are compared to these alternative editing schemes using several synthetic and real data problems. The empirical results show that, the proposed editing algorithm constitutes a good trade-off among performance and computational burden.
From the nearest neighbour rule to decision trees
1998
This paper proposes an algorithm to design a tree-like classifier whose result is equivalent to that achieved by the classical Nearest Neighbour rule. The procedure consists of a particular decomposition of a d-dimensional feature space into a set of convex regions with prototypes from just one class. Some experimental results over synthetic and real databases are provided in order to illustrate the applicability of the method.
An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains
2021
Understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience. However, an optimal approach of assessing these interactions has not been established, as existing methods either do not consider the inherent point process nature of spike trains or are based on parametric assumptions that may lead to wrong inferences if not met. This work presents a framework, grounded in the field of information dynamics, for the model-free, continuous-time estimation of both undirected (symmetric) and directed (causal) interactions between pairs of spike trains. The framework decomposes the overall information exchanged dynami…
An evolutionary restricted neighborhood search clustering approach for PPI networks
2014
Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions intr…
Using proximity and spatial homogeneity in neighbourhood-based classifiers
1997
In this paper, a set of neighbourhood-based classifiers are jointly used in order to select a more reliable neighbourhood of a given sample and take an appropriate decision about its class membership. The approaches introduced here make use of two concepts: proximity and symmetric placement of the samples.
Protein data condensation for effective quaternary structure classification
2007
Many proteins are composed of two or more subunits, each associated with different polypeptide chains. The number and the arrangement of subunits forming a protein are referred to as quaternary structure. The quaternary structure of a protein is important, since it characterizes the biological function of the protein when it is involved in specific biological processes. Unfortunately, quaternary structures are not trivially deducible from protein amino acid sequences. In this work, we propose a protein quaternary structure classification method exploiting the functional domain composition of proteins. It is based on a nearest neighbor condensation technique in order to reduce both the porti…
Local Feature Selection with Dynamic Integration of Classifiers
2000
Multidimensional data is often feature space heterogeneous so that individual features have unequal importance in different sub areas of the feature space. This motivates to search for a technique that provides a strategic splitting of the instance space being able to identify the best subset of features for each instance to be classified. Our technique applies the wrapper approach where a classification algorithm is used as an evaluation function to differentiate between different feature subsets. In order to make the feature selection local, we apply the recent technique for dynamic integration of classifiers. This allows to determine which classifier and which feature subset should be us…
A one class KNN for signal identification: a biological case study
2009
The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes harder whenever only one pattern is well-represented in the signal; in such cases, one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a multi layer model (MLM) that provides preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
An improved distance-based relevance feedback strategy for image retrieval
2013
Most CBIR (content based image retrieval) systems use relevance feedback as a mechanism to improve retrieval results. NN (nearest neighbor) approaches provide an efficient method to compute relevance scores, by using estimated densities of relevant and non-relevant samples in a particular feature space. In this paper, particularities of the CBIR problem are exploited to propose an improved relevance feedback algorithm based on the NN approach. The resulting method has been tested in a number of different situations and compared to the standard NN approach and other existing relevance feedback mechanisms. Experimental results evidence significant improvements in most cases.
Interactive Image Retrieval Using Smoothed Nearest Neighbor Estimates
2010
Relevance feedback has been adopted by most recent Content Based Image Retrieval systems to reduce the semantic gap that exists between the subjective similarity among images and the similarity measures computed in a given feature space. Distance-based relevance feedback using nearest neighbors has been recently presented as a good tradeoff between simplicity and performance. In this paper, we analyse some shortages of this technique and propose alternatives that help improving the efficiency of the method in terms of the retrieval precision achieved. The resulting method has been evaluated on several repositories which use different feature sets. The results have been compared to those obt…