79,397 research outputs found
Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks
This paper addresses the problem of classifying materials from microspectroscopy at a pixel level. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels. We approach the problem by designing a supervised classifier from a tandem of Artificial Neural Network (ANN) models that identify relevant features in raw spectra and achieve high classification accuracy. The tandem of ANN models is meshed with classification rule extraction methods to lower the model complexity and to achieve interpretability of the resulting model. The contribution of the work is in designing each ANN model based on the microspectroscopy hypothesis about a discriminatory feature of a certain target class being composed of a linear combination of spectra. The novelty lies in meshing ANN and decision rule models into a tandem configuration to achieve accurate and interpretable classification results. The proposed method was evaluated using a set of broadband coherent anti-Stokes Raman scattering (BCARS) microscopy cell images (600 000 pixel-level spectra) and a reference four-class rule-based model previously created by biochemical experts. The generated classification rule-based model was on average 85% accurate measured by the DICE pixel label similarity metric, and on average 96% similar to the reference rules measured by the vector cosine metric
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Learning distance to subspace for the nearest subspace methods in high-dimensional data classification
The nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional data. In this paper, we propose to improve the classification performance of NSM through learning tailored distance metrics from samples to class subspaces. The learned distance metric is termed as ‘learned distance to subspace’ (LD2S). Using LD2S in the classification rule of NSM can make the samples closer to their correct class subspaces while farther away from their wrong class subspaces. In this way, the classification task becomes easier and the classification performance of NSM can be improved. The superior classification performance of using LD2S for NSM is demonstrated on three real-world high-dimensional spectral datasets
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations
The use of numerical uncertainty representations allows better modeling of
some aspects of human evidential reasoning. It also makes knowledge acquisition
and system development, test, and modification more difficult. We propose that
where possible, the assignment and/or refinement of rule weights should be
performed automatically. We present one approach to performing this training -
numerical optimization - and report on the results of some preliminary tests in
training rule bases. We also show that truth maintenance can be used to make
training more efficient and ask some epistemological questions raised by
training rule weights.Comment: Appears in Proceedings of the Third Conference on Uncertainty in
Artificial Intelligence (UAI1987
Inducing Generalized Multi-Label Rules with Learning Classifier Systems
In recent years, multi-label classification has attracted a significant body
of research, motivated by real-life applications, such as text classification
and medical diagnoses. Although sparsely studied in this context, Learning
Classifier Systems are naturally well-suited to multi-label classification
problems, whose search space typically involves multiple highly specific
niches. This is the motivation behind our current work that introduces a
generalized multi-label rule format -- allowing for flexible label-dependency
modeling, with no need for explicit knowledge of which correlations to search
for -- and uses it as a guide for further adapting the general Michigan-style
supervised Learning Classifier System framework. The integration of the
aforementioned rule format and framework adaptations results in a novel
algorithm for multi-label classification whose behavior is studied through a
set of properly defined artificial problems. The proposed algorithm is also
thoroughly evaluated on a set of multi-label datasets and found competitive to
other state-of-the-art multi-label classification methods
The DD-classifier in the functional setting
The Maximum Depth was the first attempt to use data depths instead of
multivariate raw data to construct a classification rule. Recently, the
DD-classifier has solved several serious limitations of the Maximum Depth
classifier but some issues still remain. This paper is devoted to extending the
DD-classifier in the following ways: first, to surpass the limitation of the
DD-classifier when more than two groups are involved. Second to apply regular
classification methods (like NN, linear or quadratic classifiers, recursive
partitioning,...) to DD-plots to obtain useful insights through the diagnostics
of these methods. And third, to integrate different sources of information
(data depths or multivariate functional data) in a unified way in the
classification procedure. Besides, as the DD-classifier trick is especially
useful in the functional framework, an enhanced revision of several functional
data depths is done in the paper. A simulation study and applications to some
classical real datasets are also provided showing the power of the new
proposal.Comment: 29 pages, 6 figures, 6 tables, Supplemental R Code and Dat
A Distributed Approach towards Discriminative Distance Metric Learning
Distance metric learning is successful in discovering intrinsic relations in
data. However, most algorithms are computationally demanding when the problem
size becomes large. In this paper, we propose a discriminative metric learning
algorithm, and develop a distributed scheme learning metrics on moderate-sized
subsets of data, and aggregating the results into a global solution. The
technique leverages the power of parallel computation. The algorithm of the
aggregated distance metric learning (ADML) scales well with the data size and
can be controlled by the partition. We theoretically analyse and provide bounds
for the error induced by the distributed treatment. We have conducted
experimental evaluation of ADML, both on specially designed tests and on
practical image annotation tasks. Those tests have shown that ADML achieves the
state-of-the-art performance at only a fraction of the cost incurred by most
existing methods
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier
Intrusion detection system (IDS) is one of extensively used techniques in a
network topology to safeguard the integrity and availability of sensitive
assets in the protected systems. Although many supervised and unsupervised
learning approaches from the field of machine learning have been used to
increase the efficacy of IDSs, it is still a problem for existing intrusion
detection algorithms to achieve good performance. First, lots of redundant and
irrelevant data in high-dimensional datasets interfere with the classification
process of an IDS. Second, an individual classifier may not perform well in the
detection of each type of attacks. Third, many models are built for stale
datasets, making them less adaptable for novel attacks. Thus, we propose a new
intrusion detection framework in this paper, and this framework is based on the
feature selection and ensemble learning techniques. In the first step, a
heuristic algorithm called CFS-BA is proposed for dimensionality reduction,
which selects the optimal subset based on the correlation between features.
Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF),
and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting
technique is used to combine the probability distributions of the base learners
for attack recognition. The experimental results, using NSL-KDD, AWID, and
CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able
to exhibit better performance than other related and state of the art
approaches under several metrics.Comment: To be published in Computer Networks at
https://doi.org/10.1016/j.comnet.2020.10724
SkILL - a Stochastic Inductive Logic Learner
Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored
area of Statistical Relational Learning which extends classic Inductive Logic
Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic
Learner, which takes probabilistic annotated data and produces First Order
Logic theories. Data in several domains such as medicine and bioinformatics
have an inherent degree of uncer- tainty, that can be used to produce models
closer to reality. SkILL can not only use this type of probabilistic data to
extract non-trivial knowl- edge from databases, but it also addresses
efficiency issues by introducing a novel, efficient and effective search
strategy to guide the search in PILP environments. The capabilities of SkILL
are demonstrated in three dif- ferent datasets: (i) a synthetic toy example
used to validate the system, (ii) a probabilistic adaptation of a well-known
biological metabolism ap- plication, and (iii) a real world medical dataset in
the breast cancer domain. Results show that SkILL can perform as well as a
deterministic ILP learner, while also being able to incorporate probabilistic
knowledge that would otherwise not be considered
Causal nearest neighbor rules for optimal treatment regimes
The estimation of optimal treatment regimes is of considerable interest to
precision medicine. In this work, we propose a causal -nearest neighbor
method to estimate the optimal treatment regime. The method roots in the
framework of causal inference, and estimates the causal treatment effects
within the nearest neighborhood. Although the method is simple, it possesses
nice theoretical properties. We show that the causal -nearest neighbor
regime is universally consistent. That is, the causal -nearest neighbor
regime will eventually learn the optimal treatment regime as the sample size
increases. We also establish its convergence rate. However, the causal
-nearest neighbor regime may suffer from the curse of dimensionality, i.e.
performance deteriorates as dimensionality increases. To alleviate this
problem, we develop an adaptive causal -nearest neighbor method to perform
metric selection and variable selection simultaneously. The performance of the
proposed methods is illustrated in simulation studies and in an analysis of a
chronic depression clinical trial
Impostor Networks for Fast Fine-Grained Recognition
In this work we introduce impostor networks, an architecture that allows to
perform fine-grained recognition with high accuracy and using a light-weight
convolutional network, making it particularly suitable for fine-grained
applications on low-power and non-GPU enabled platforms. Impostor networks
compensate for the lightness of its `backend' network by combining it with a
lightweight non-parametric classifier. The combination of a convolutional
network and such non-parametric classifier is trained in an end-to-end fashion.
Similarly to convolutional neural networks, impostor networks can fit
large-scale training datasets very well, while also being able to generalize to
new data points. At the same time, the bulk of computations within impostor
networks happen through nearest neighbor search in high-dimensions. Such search
can be performed efficiently on a variety of architectures including standard
CPUs, where deep convolutional networks are inefficient. In a series of
experiments with three fine-grained datasets, we show that impostor networks
are able to boost the classification accuracy of a moderate-sized convolutional
network considerably at a very small computational cost
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