Multivariate time series classification is a machine learning task with increasing importance due to the proliferation of information sources in different domains (economy, health, energy, crops, etc.). Univariate methods lack ability capture relationships between variables that compose multivariate and therefore cannot be directly extrapolated environments. Despite good performance competitive...

Multivariate temporal data are hybrid data. Numeric and categorical data type could be consisted of. Most past researches cannot be operated directly on the multivariate temporal data with both types. Additionally, no useful and readable rules are provided in their methods for advanced classification analysis. We proposed Progressive Temporal Class Rule Miner (PTCR-Miner) algorithm to achieve t...

Kernel-based approaches for sequence classification have been successfully applied to a variety of domains, including the text categorization, image classification, speech analysis, biological sequence analysis, time series and music classification, where they show some of the most accurate results. Typical kernel functions for sequences in these domains (e.g., bag-of-words, mismatch, or subseq...

Machine learning research has, to a great extent, ignored an important aspect of many real world applications: time. Existing concept learners predominantly operate on a static set of attributes; for example, classifying flowers described by leaf size, petal colour and petal count. The values of these attributes is assumed to be unchanging – the flower never grows or loses leaves. However, many...

Smells can be described by assigning the words that come to mind when sniffing an odorous material. A great number of terms applied, but not all them are independent, and it is possible establish groups often applied together describing a smell. Such classification olfactory descriptors scientific interest in order better understand dimensionality structure human perception space. For this purp...