1 research outputs found
SVM-Lattice: A Recognition & Evaluation Frame for Double-peaked Profiles
In big data era, the special data with rare characteristics may be of great
significations. However, it is very difficult to automatically search these
samples from the massive and high-dimensional datasets and systematically
evaluate them. The DoPS, our previous work [2], provided a search method of
rare spectra with double-peaked profiles from massive and high-dimensional data
of LAMOST survey. The identification of the results is mainly depended on
visually inspection by astronomers. In this paper, as a follow-up study, a new
lattice structure named SVM-Lattice is designed based on SVM(Support Vector
Machine) and FCL(Formal Concept Lattice) and particularly applied in the
recognition and evaluation of rare spectra with double-peaked profiles. First,
each node in the SVM-Lattice structure contains two components: the intents are
defined by the support vectors trained by the spectral samples with the
specific characteristics, and the relevant extents are all the positive samples
classified by the support vectors. The hyperplanes can be extracted from every
lattice node and used as classifiers to search targets by categories. A
generalization and specialization relationship is expressed between the layers,
and higher layers indicate higher confidence of targets. Then, including a
SVM-Lattice building algorithm, a pruning algorithm based on association rules,
and an evaluation algorithm, the supporting algorithms are provided and
analysed. Finally, for the recognition and evaluation of spectra with
double-peaked profiles, several data sets from LAMOST survey are used as
experimental dataset. The results exhibit good consistency with traditional
methods, more detailed and accurate evaluations of classification results, and
higher searching efficiency than other similar methods.Comment: 19 pages,11 figures,IEEE Access accepte