1,926 research outputs found
Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm
Hyperspectral images (HIS) classification is a high technical remote sensing
tool. The goal is to reproduce a thematic map that will be compared with a
reference ground truth map (GT), constructed by expecting the region. The HIS
contains more than a hundred bidirectional measures, called bands (or simply
images), of the same region. They are taken at juxtaposed frequencies.
Unfortunately, some bands contain redundant information, others are affected by
the noise, and the high dimensionality of features made the accuracy of
classification lower. The problematic is how to find the good bands to classify
the pixels of regions. Some methods use Mutual Information (MI) and threshold,
to select relevant bands, without treatment of redundancy. Others control and
eliminate redundancy by selecting the band top ranking the MI, and if its
neighbors have sensibly the same MI with the GT, they will be considered
redundant and so discarded. This is the most inconvenient of this method,
because this avoids the advantage of hyperspectral images: some precious
information can be discarded. In this paper we'll accept the useful redundancy.
A band contains useful redundancy if it contributes to produce an estimated
reference map that has higher MI with the GT.nTo control redundancy, we
introduce a complementary threshold added to last value of MI. This process is
a Filter strategy; it gets a better performance of classification accuracy and
not expensive, but less preferment than Wrapper strategy.Comment: 11 pages, 5 figures, journal pape
Ensembles of wrappers for automated feature selection in fish age classification
In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed.Peer ReviewedPostprint (author's final draft
Generating Compact Tree Ensembles via Annealing
Tree ensembles are flexible predictive models that can capture relevant
variables and to some extent their interactions in a compact and interpretable
manner. Most algorithms for obtaining tree ensembles are based on versions of
boosting or Random Forest. Previous work showed that boosting algorithms
exhibit a cyclic behavior of selecting the same tree again and again due to the
way the loss is optimized. At the same time, Random Forest is not based on loss
optimization and obtains a more complex and less interpretable model. In this
paper we present a novel method for obtaining compact tree ensembles by growing
a large pool of trees in parallel with many independent boosting threads and
then selecting a small subset and updating their leaf weights by loss
optimization. We allow for the trees in the initial pool to have different
depths which further helps with generalization. Experiments on real datasets
show that the obtained model has usually a smaller loss than boosting, which is
also reflected in a lower misclassification error on the test set.Comment: Comparison with Random Forest included in the results sectio
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural Network Classifier
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy
Preparation of Silver Decorated Reduced Graphene Oxide Nanohybrid for Effective Photocatalytic Degradation of Indigo Carmine Dye
Background: Even though silver decorated reduced graphene oxide (Ag-rGO) shows max-
imum absorptivity in the UV region, most of the research on the degradation of dyes using Ag-rGO is
in the visible region. Therefore the present work focused on the photocatalytic degradation of indigo
carmine (IC) dye in the presence of Ag-rGO as a catalyst by UV light irradiation.
Methods: In this context, silver-decorated reduced graphene oxide hybrid material was fabricated and
explored its potential for the photocatalytic degradation of aqueous IC solution in the UV region. The
decoration of Ag nanoparticles on the surface of the rGO nanosheets is evidenced by TEM analysis.
The extent of mineralization of the dye was measured by estimating chemical oxygen demand (COD)
values before and after irradiation.
Results: The synthesized Ag-rGO binary composites displayed excellent photocatalytic activity in 2
Χ 10-5 M IC concentration and 5mg catalyst loading. The optical absorption spectrum of Ag-rGO
showed that the energy band-gap was found to be 2.27 eV, which is significantly smaller compared to
the band-gap of GO. 5 mg of Ag-rGO was found to be an optimum quantity for the effective degrada-
tion of IC dye. The degradation rate increases with the decrease in the concentration of the dye at al-
kaline pH conditions. The photocatalytic efficiency was 92% for the second time.
Conclusion: The impact of the enhanced reactive species generation was consistent with higher pho-
tocatalytic dye degradation. The photocatalytic mechanism has been proposed and the hydroxyl radi-
cal was found to be the reactive species responsible for the degradation of dye. The feasibility of reus-
ing the photocatalyst showed that the photocatalytic efficiency was very effective for the second tim
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