3,031 research outputs found
Classifiers accuracy improvement based on missing data imputation
In this paper we investigate further and extend our previous work on radar signal identification
and classification based on a data set which comprises continuous, discrete and
categorical data that represent radar pulse train characteristics such as signal frequencies,
pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the
most of the real world datasets, it also contains high percentage of missing values and
to deal with this problem we investigate three imputation techniques: Multiple Imputation
(MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI).
We apply these methods to data samples with up to 60% missingness, this way doubling
the number of instances with complete values in the resulting dataset. The imputation
models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s
effect size metrics. To solve the classification task, we employ three intelligent approaches:
Neural Networks (NN); Support Vector Machines (SVM); and Random Forests
(RF). Subsequently, we critically analyse which imputation method influences most the
classifiers’ performance, using a multiclass classification accuracy metric, based on the
area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each
containing several ‘subclasses’, and introduce and propose two new metrics: inner class
accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy
(OCA) metric. We conclude that they can be used as complementary to the OCA
when choosing the best classifier for the problem at hand
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
A Constructive, Incremental-Learning Network for Mixture Modeling and Classification
Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a Gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known Expectation-Maximization (EM) approach to mixture-modeling. GAM outperforms an EM classification algorithm on a classification benchmark, thereby demonstrating the advantage of the ART match criterion for regulating learning, and the ARTMAP match tracking operation for incorporate environmental feedback in supervised learning situations.Office of Naval Research (N00014-95-1-0409
A Constructive, Incremental-Learning Network for Mixture Modeling and Classification
Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses Gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a Gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known Expectation-Maximization (EM) approach to mixture-modeling. GAM outperforms an EM classification algorithm on a classification benchmark, thereby demonstrating the advantage of the ART match criterion for regulating learning, and the ARTMAP match tracking operation for incorporate environmental feedback in supervised learning situations.Office of Naval Research (N00014-95-1-0409
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving
To mitigate the challenges arising from partial occlusion in human pose
keypoint based pedestrian detection methods , we present a novel pedestrian
pose keypoint completion method called the separation and dimensionality
reduction-based generative adversarial imputation networks (SDR-GAIN) .
Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we
isolate the head and torso keypoints of pedestrians with incomplete keypoints
due to occlusion or other factors and perform dimensionality reduction to
enhance features and further unify feature distribution. Finally, we introduce
two generative models based on the generative adversarial networks (GAN)
framework, which incorporate Huber loss, residual structure, and L1
regularization to generate missing parts of the incomplete head and torso pose
keypoints of partially occluded pedestrians, resulting in pose completion. Our
experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms
basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning
methods k-NN and MissForest in terms of pose completion task. In addition, the
runtime of SDR-GAIN is approximately 0.4ms, displaying high real-time
performance and significant application value in the field of autonomous
driving
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