Panahi, M. S., Yousefi, A. K., and Khorshidi, M. (2013). Combining accuracy and success-rate to improve the performance of eXtended Classifier System (XCS) for data-mining and control applications. Engineering Applications of Artificial Intelligence, 26(8): p. 1930-1935....
classifier n. 1. 分类成分,分类词(能显示同义所属关系,如前缀 un 是表示反义的分类成分)an affix or word which shows that a word belongs to a group of words with similar meanings. For example the prefix ‘un’ is a classifier that shows the word is negative. ...
Beigy, "Using a classifier pool in accuracy based tracking of recurring concepts in data stream classifica- tion," Evolving Systems, vol. 4, no. 1, pp. 43-60, 2013.M. J. Hosseini, Z. Ahmadi, and H. Beigy, "Using a classifier pool in accuracy based tracking of recurring concepts ...
2.classifier- a word or morpheme used in some languages in certain contexts (such as counting) to indicate the semantic class to which the counted item belongs word- a unit of language that native speakers can identify; "words are the blocks from which sentences are made"; "he hardly said...
A previous blog post, The Basics of Classifier Evaluation, Part 1, made the point that classifiers shouldn’t use classification accuracy — that is, the portion of labels predicted correctly — as a performance metric. There are several good reasons for avoiding accuracy, having to do with cla...
The era of data mining has provided renewed effort in the research of certain areas of biology that for their difficulty and lack of knowledge were and are still considered unsolved problems. One such problem, which is one of the fundamental open problem
Hence if a decision tree is developed for any disease then accuracy is calculated that how much the decision is correct. Entropy (class): It is also called Shanon Entropy and is denoted by H(s) for a finite set S. It shows the evaluation of the amount of unpredictability in data. Here...
Bayesian classifier assists in finding the probability that a given instance (a record) belong to a particular class. This statistical classifier exhibits high accuracy and speed in mining large databases. The working principle of the Bayesian classifier is based on the assumption (class condition ...
Matthew’s Correlation Coefficient (MCC) is another measure of binary classification performance34that has been extended to handle multi-class problems35. Its main merit is in taking into account true negatives (accuracy or\({F}_{1}\)do not), which makes MCC especially useful when negative exa...
it is critical in these applications to have a classifier that scales well and can handle training data of this magnitude. As an additional advantage, being able to classify large training data also leads to an improvement in the classification accuracy. Such a result was demonstrated, for exampl...