System and method for identifying an object
AssigneeOhio University
InventorsJanusz Starzyk, Dale Nelson
Life of this patent
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Abstract
A system and method for determining the classification of a signal, or the identification of an object is provided. Based on rough set theory, or data mining, a training data set is partitioned and labeled with a multi-class entropy method. Reducts are calculated from a subset of the best-performing columns of the partitioned and labeled training set data. These reducts are applied to test signals and combined for each signal classification. The present system and method produces a more accurate, robust and efficient classification result.
Claims26
1-32. (canceled)
A system for determining the classification of a signal comprising:
- a plurality of training signals associated with one or more known classifications, wherein each of the training signals has a plurality of training signal attributes;
- a first reduct calculator for calculating one or more reducts that include the minimum number of training signal attributes that are required to correctly classify the plurality training signals;
- a classifier for classifying a plurality of unclassified signals as a function of the one or more reducts and one or more attributes of the unclassified signals;
- a marginal reduct identifier for determining if one or more reducts provided one or more marginal classifications;
- and
- a reduct classification fuser for combining one or more marginal reducts identified by the marginal reduct identifier.
A method of determining to what classification a signal belongs, the method comprising the steps of:
- providing a classification system including a subset of training signals and a plurality of reducts, the classification system having been determined by the steps of:
- providing a training information set in the form of a table of data, the table having a plurality of columns and rows, wherein each column of the table represents an attribute of the classification and wherein each row of the table is a training signal and represents attributes associated with a specific classification;
- partitioning the training signals;
- wavelet transforming signals in each of the training signal partitions and appending wavelet coefficients as additional signal values;
- labeling signals resulting from the wavelet transforming step;
- selecting a subset of the plurality of columns by removing duplicate and ambiguous signals;
- calculating the plurality of reducts;
- providing a set of test signals in the form of a table with a plurality of columns of attributes and rows of test signals;
- determining a classification of each test signal of the plurality of test signals using the classification system.
Dependencies are inferred from the claim text.