Name
Abstract | ||
|---|---|---|
A new method is proposed for classifying sets of a variable number of points and curves in a multidimensional space as time series. Almost all classifiers proposed so far assume that there is a constant number of features and they cannot treat a variable number of features. To cope with this difficulty, we examine a fixed number of questions like “how many points are in a certain range of a certain dimension”, and we convert the corresponding answers into a binary vector with a fixed length. These converted binary vectors are used as the basis for our classification. With respect to curve classification, many conventional methods are based on a frequency analysis such as Fourier analysis, a predictive analysis such as auto-regression, or a hidden Markov model. However, their resulting classification rules are difficult to interpret. In addition, they also rely on the global shape of curves and cannot treat cases in which only one part of a curve is important for classification. We propose some methods that are especially effective for such cases and the obtained rule is visualized. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0167-8655(99)00077-X | Pattern Recognition Letters |
Keywords | Field | DocType |
classification of sets of multidimensional points,multidimensional curve classification,binarization,subclass method,variable number of features,time series,auto regressive,frequency analysis,fourier analysis,hidden markov model | Fourier analysis,Classification rule,Pattern recognition,Artificial intelligence,Hidden Markov model,Frequency analysis,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
20 | 11-13 | Pattern Recognition Letters |
Citations | PageRank | References |
47 | 4.55 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mineichi Kudo | 1 | 927 | 116.09 |
| Jun Toyama | 2 | 130 | 19.87 |
| Masaru Shimbo | 3 | 179 | 33.02 |