48,519 research outputs found
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
The k-Nearest Neighbor (kNN) classification approach is conceptually simple -
yet widely applied since it often performs well in practical applications.
However, using a global constant k does not always provide an optimal solution,
e.g., for datasets with an irregular density distribution of data points. This
paper proposes an adaptive kNN classifier where k is chosen dynamically for
each instance (point) to be classified, such that the expected accuracy of
classification is maximized. We define the expected accuracy as the accuracy of
a set of structurally similar observations. An arbitrary similarity function
can be used to find these observations. We introduce and evaluate different
similarity functions. For the evaluation, we use five different classification
tasks based on geo-spatial data. Each classification task consists of (tens of)
thousands of items. We demonstrate, that the presented expected accuracy
measures can be a good estimator for kNN performance, and the proposed adaptive
kNN classifier outperforms common kNN and previously introduced adaptive kNN
algorithms. Also, we show that the range of considered k can be significantly
reduced to speed up the algorithm without negative influence on classification
accuracy
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
K x N Trust-Based Agent Reputation
In this research, a multi-agent system called KMAS is presented that models an environment of intelligent, autonomous, rational, and adaptive agents that reason about trust, and adapt trust based on experience. Agents reason and adapt using a modification of the k-Nearest Neighbor algorithm called (k X n) Nearest Neighbor where k neighbors recommend reputation values for trust during each of n interactions. Reputation allows a single agent to receive recommendations about the trustworthiness of others. One goal is to present a recommendation model of trust that outperforms MAS architectures relying solely on direct agent interaction. A second goal is to converge KMAS to an emergent system state where only successful cooperation is allowed. Three experiments are chosen to compare KMAS against a non-(k X n) MAS, and between different variations of KMAS execution. Research results show KMAS converges to the desired state, and in the context of this research, KMAS outperforms a direct interaction-based system
Adaptive Multi-level Backward Tracking for Sequential Feature Selection
In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets
Certified data-driven physics-informed greedy auto-encoder simulator
A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI)
framework is developed for accurate, efficient, and certified data-driven
physics-informed greedy auto-encoder simulators of high-dimensional nonlinear
dynamical systems. In the proposed framework, an auto-encoder and dynamics
identification models are trained interactively to discover intrinsic and
simple latent-space dynamics. To effectively explore the parameter space for
optimal model performance, an adaptive greedy sampling algorithm integrated
with a physics-informed error indicator is introduced to search for optimal
training samples on the fly, outperforming the conventional predefined uniform
sampling. Further, an efficient k-nearest neighbor convex interpolation scheme
is employed to exploit local latent-space dynamics for improved predictability.
Numerical results demonstrate that the proposed method achieves 121 to 2,658x
speed-up with 1 to 5% relative errors for radial advection and 2D Burgers
dynamical problems.Comment: arXiv admin note: substantial text overlap with arXiv:2204.1200
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