Abstract
AbstractIn this paper, we describe the fingerprint method, a technique to classify bags of mixed-type measurements. The method was designed to solve a real-world industrial problem: classifying industrial plants (individuals at a higher level of organisation) starting from the measurements collected from their production lines (individuals at a lower level of organisation). In this specific application, the categorical information attached to the numerical measurements induced simple mixture-like structures on the global multivariate distributions associated with different classes. The fingerprint method is designed to compare the mixture components of a given test bag with the corresponding mixture components associated with the different classes, identifying the most similar generating distribution. When compared to other classification algorithms applied to several synthetic data sets and the original industrial data set, the proposed classifier showed remarkable improvements in performance.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Statistics and Probability
Reference25 articles.
1. Abdullin A, Nasraoui O (2012) Clustering heterogeneous data sets. In: Proceedings of the 2012 Eighth Latin American Web Congress, IEEE
2. Ahmad A, Dey L (2007) A $$k$$-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 63:503–527
3. Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems 15, Neural Information Processing Systems (NIPS)
4. Biernacki C, Deregnaucourt T, Kubicki V (2015) Model-based clustering with mixed/missing data using the new software MixtComp. In: 8th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics), ERCIM
5. Blizard WD (1991) The development of multiset theory. Modern Logic 1:319–352