54,341 research outputs found
A Survey of Classification Methods
Classification may refer to categorization, the process in which ideas and objects are recognized, differentiated, and understood. There are many types of classification, researchers face a problem to choose a suitable method that give a good classification performance to solve their classification problems. In this paper, we present the basic classification techniques. Several major kinds of classification method including neural network, decision tree, Bayesian networks, support vector machine and k-nearest neighbor classifier. The goal of this survey is to provide a comprehensive review of the above different classification techniques
Post-correlation radio frequency interference classification methods
We describe and compare several post-correlation radio frequency interference
classification methods. As data sizes of observations grow with new and
improved telescopes, the need for completely automated, robust methods for
radio frequency interference mitigation is pressing. We investigated several
classification methods and find that, for the data sets we used, the most
accurate among them is the SumThreshold method. This is a new method formed
from a combination of existing techniques, including a new way of thresholding.
This iterative method estimates the astronomical signal by carrying out a
surface fit in the time-frequency plane. With a theoretical accuracy of 95%
recognition and an approximately 0.1% false probability rate in simple
simulated cases, the method is in practice as good as the human eye in finding
RFI. In addition it is fast, robust, does not need a data model before it can
be executed and works in almost all configurations with its default parameters.
The method has been compared using simulated data with several other mitigation
techniques, including one based upon the singular value decomposition of the
time-frequency matrix, and has shown better results than the rest.Comment: 14 pages, 12 figures (11 in colour). The software that was used in
the article can be downloaded from http://www.astro.rug.nl/rfi-software
A review of electricity load profile classification methods
With the electricity market liberalisation in Indonesia, the electricity companies will have the right to develop tariff rates independently. Thus, precise knowledge of load profile classifications of customers will become essential for designing a variety of tariff options, in which the tariff rates are in line with efficient revenue generation and will encourage optimum take up of the available electricity supplies, by various types of customers. Since the early days of the liberalisation of the Electricity Supply Industries (ESI) considerable efforts have been made to investigate methodologies to form optimal tariffs based on customer classes, derived from various clustering and classification techniques. Clustering techniques are analytical processes which are used to develop groups (classes) of customers based on their behaviour and to derive representative sets of load profiles and help build models for daily load shapes. Whereas classification techniques are processes that start by analysing load demand data (LDD) from various customers and then identify the groups that these customers' LDD fall into. In this paper we will review some of the popular clustering algorithms, explain the difference between each method
Classification methods for Hilbert data based on surrogate density
An unsupervised and a supervised classification approaches for Hilbert random
curves are studied. Both rest on the use of a surrogate of the probability
density which is defined, in a distribution-free mixture context, from an
asymptotic factorization of the small-ball probability. That surrogate density
is estimated by a kernel approach from the principal components of the data.
The focus is on the illustration of the classification algorithms and the
computational implications, with particular attention to the tuning of the
parameters involved. Some asymptotic results are sketched. Applications on
simulated and real datasets show how the proposed methods work.Comment: 33 pages, 11 figures, 6 table
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