Rule-based Classification for Audio Data Based on Closed Itemset Mining
Takahiro Tada, Tomomasa Nagashima, Yoshifumi Okada · 2010
Abstract — Automatic audio classification is a major topic in the fields of pattern recognition and data-mining. This paper describes a new rule-based classification method (cREAD: classification Rule Extraction for Audio Data) for multi-class audio data. Typically, rule-based classification requires considerable computation cost to find rules from large datasets because of the combinatorial search problem. To achieve efficient and fast extraction of classification rules, we take advantage of a closed itemset mining algorithm that can exhaustively extract non-redundant and condensed patterns from a transaction database within a reasonable time. The notable feature of this method is that the search space of classification rules can be dramatically reduced by searching for only closed itemsets constrained by “class label item. ” In this paper, we show that our method is superior to the other salient methods in its classification accuracy of a real audio dataset with the “underdetermined problem ” that the number of samples is overwhelmingly fewer than that of attributes. Index Terms—audio data, rule-based classification, closed itemset, LCM. I.