11,892 research outputs found
Random Prism: An Alternative to Random Forests.
Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prismās classification accuracy by reducing overfitting
A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform
Hyperledger Fabric (HLF) is a flexible permissioned blockchain platform
designed for business applications beyond the basic digital coin addressed by
Bitcoin and other existing networks. A key property of HLF is its
extensibility, and in particular the support for multiple ordering services for
building the blockchain. Nonetheless, the version 1.0 was launched in early
2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering
service. To overcome this limitation, we designed, implemented, and evaluated a
BFT ordering service for HLF on top of the BFT-SMaRt state machine
replication/consensus library, implementing also optimizations for wide-area
deployment. Our results show that HLF with our ordering service can achieve up
to ten thousand transactions per second and write a transaction irrevocably in
the blockchain in half a second, even with peers spread in different
continents
Multispectral Palmprint Recognition Using Textural Features
In order to utilize identification to the best extent, we need robust and
fast algorithms and systems to process the data. Having palmprint as a reliable
and unique characteristic of every person, we extract and use its features
based on its geometry, lines and angles. There are countless ways to define
measures for the recognition task. To analyze a new point of view, we extracted
textural features and used them for palmprint recognition. Co-occurrence matrix
can be used for textural feature extraction. As classifiers, we have used the
minimum distance classifier (MDC) and the weighted majority voting system
(WMV). The proposed method is tested on a well-known multispectral palmprint
dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most
scenarios which outperforms all previous works in multispectral palmprint
recognition.Comment: 5 pages, Published in IEEE Signal Processing in Medicine and Biology
Symposium 201
Simulating Wde-area Replication
We describe our experiences with simulating replication algorithms for use in far flung distributed systems. The algorithms under scrutiny mimic epidemics. Epidemic algorithms seem to scale and adapt to change (such as varying replica sets) well. The loose consistency guarantees they make seem more useful in applications where availability strongly outweighs correctness; e.g., distributed name service
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