Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems
The quality of the cases maintained in a case base has a direct influence on the quality of the proposed solutions. The presence of cases that do not conform to the similarity hypothesis decreases the alignment of the case base and often degrades the performance of a CBR system. It is therefore important to find out the suitability of each case for the application of CBR and associate a solution with a certain degree of confidence. Feature weighting is another important aspect that determines the success of a system, as the presence of irrelevant and redundant attributes also results in incorrect solutions. We explore these problems in conjunction with a real-world CBR application called InfoChrom. It is used to predict the values of several soil nutrients based on features extracted from a chromatogram image of a soil sample. We propose novel feature weighting techniques based on alignment, as well as a new alignment and confidence measure as potential solutions. The hypotheses are evaluated on UCI datasets and the case base of Infochrom and show promising results.
KeywordsMutual Information Case Base Class Label Target Variable Principal Component Regression
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