196,340 research outputs found
Dissimilarity Random Forest Clustering
No full textIn this paper we present DisRFC (Dissimilarity Random Forest Clustering), a novel Random Forest Clustering approach which, contrarily to current methods which require in input a vectorial representation, works only with dissimilarities, thus being applicable also to all those problems where a vectorial representation is not available but a descriptive dissimilarity measure can be computed. In the DisRFC approach objects to be clustered are first modelled with a novel RF variant called Unsupervised Dissimilarity Random Forest (UD-RF), which functioning mechanisms are both unsupervised and based on dissimilarities. The trained UD-RF is then used to project objects in a binary vectorial space, where effective K-means procedures can be used to obtain the final clustering. In the paper we present different variants of DisRFC, thoroughly and positively evaluated using 10 different problems
DisRFC: a dissimilarity-based Random Forest Clustering approach
No full textIn this paper we present a novel Random Forest Clustering approach, called Dissimilarity Random Forest Clustering (DisRFC), which requires in input only pairwise dissimilarities. Thanks to this characteristic, the proposed approach is appliable to all those problems which involve non-vectorial representations, such as strings, sequences, graphs or 3D structures. In the proposed approach, we first train an Unsupervised Dis-similarity Random Forest (UD-RF), a novel variant of Random Forest which is completely unsupervised and based on dissimilarities. Then, we exploit the trained UD-RF to project the patterns to be clustered in a binary vectorial space, where the clustering is finally derived using fast and effective K-means procedures. In the paper we introduce different variants of DisRFC, which are thoroughly and positively evaluated on 12 different problems, also in comparison with alternative state-of-the-art approaches.(c) 2022 Elsevier Ltd. All rights reserved
PowerHC: non linear normalization of distances for advanced nearest neighbor classification
No full textIn this paper we investigate the exploitation of non linear scaling of distances for advanced nearest neighbor classification. Starting from the recently found relation between the Hypersphere Classifier (HC) ill and the Adaptive Nearest Neighbor rule (ANN) [2], here we propose PowerHC, an improved version of HC in which distances are normalized using a non linear mapping; non linear scaling of data, whose usefulness for feature spaces has been already assessed, has been hardly investigated for distances. A thorough experimental evaluation, involving 24 datasets and a challenging real world scenario of seismic signal classification, confirms the suitability of the proposed approach
A cheaper Rectified-Nearest-Feature-Line-Segment classifier based on safe points
No full textThe Rectified Nearest Feature Line Segment (RNFLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two consecutive processes -segmentation and rectification- to the initial set of feature lines. The main drawbacks of this technique, occurring in both training and test phases, are the high computational cost of the rectification procedure and the exponential explosion of the number of lines. We propose a cheaper version of RNFLS, based on a characterization of the points that should form good lines. The characterization relies on a recent neighborhood-based principle that categorizes objects into four types: safe, borderline, rare and outliers, depending on the position of each point with respect to the other classes. The proposed approach represents a variant of RNFLS in the sense that it only considers lines between safe points. This allows a drastic reduction in the computational burden imposed by RNFLS. We carried out an empirical and thorough analysis based on different public data sets, showing that our proposed approach, in general, is not significantly different from RNFLS, but cheaper since the consideration of likely irrelevant feature line segments is avoided
Optimización de la etapa de coagulación en la elaboración de queso tybo
No full textFil: Bettiol, Marina R. Universidad Nacional de Villa María; Argentina.Fil: Piccatto, Leandro. Universidad Nacional de Villa María; Argentina.Fil: Perone, Franco A. Universidad Nacional de Villa María; Argentina.Fil: Bicego, Juan P. Universidad Nacional de Villa María; Argentina.Fil: Torasso, Héctor. Universidad Nacional de Villa María; Argentina.Fil: Berra, M. Carlos. L. Universidad Nacional de Villa María; Argentina
On the importance of local and global analysis in the judgment of similarity and dissimilarity of faces
No full textOn learning Random Forests for Random Forest-clustering
No full textIn this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context. In particular, we investigated the suitability of Gaussian Density Forests [1], Random Forests specifically designed for density estimation. Further, we introduce a novel variant of Random Forest, based on an effective non parametric by-pass estimator of the Renyi entropy, which can be useful when the parametric assumption is too strict. An empirical evaluation involving different datasets and different RF-clustering strategies confirms that the learning step is crucial for RF-clustering. We also present a set of practical guidelines useful to determine the most suitable variant of RF-clustering according to the problem under examination
Weighted K-Nearest Neighbor revisited
No full textIn this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance
Distance-Based Random Forest Clustering with~Missing Data
No full textIn recent years there has been an increased interest in clustering methods based on Random Forests, due to their flexibility and their capability in describing data. One problem of current RF-clustering approaches is that they are not able to directly deal with missing data, a common scenario in many application fields (e.g. Bioinformatics): the usual solution in this case is to pre-impute incomplete data before running standard clustering methods. In this paper we present the first Random Forest clustering approach able to directly deal with missing data. We start from the very recent RatioRF distance for clustering [3], which has shown to outperform all other distance-based RF clustering schemes, extending the framework in two directions, which allow the integration of missing data mechanisms directly inside the clustering pipeline. Experimental results, based on 6 standard UCI ML datasets, are promising, also in comparison with some literature alternatives
Watershed-based unsupervised clustering
No full textIn this paper, a novel general purpose clustering algorithm is presented, based on the watershed algorithm. The proposed approach defines a density function on a suitable lattice, whose cell dimension is carefully estimated from the data. The clustering is then performed using the well-known watershed algorithm, paying particular attention to the boundary situations. The main characteristic of this method is the capability to determine automatically the number of clusters from the data, resulting in a completely unsupervised approach. Experimental evaluation on synthetic data shows that the proposed approach is able to accurately estimate the number of the classes and to cluster data effectively
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