The automatic design of fuzzy rule-based models and classifiers from data is considered. It is recognized that both accuracy and transparency are of major importance and we seek to keep the rule-based models small and comprehensible. An iterative approach for developing such fuzzy rule-based models is proposed. First, an initial model is derived from the data. Subsequently, a real-coded genetic...

in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

This paper provides a systematic study of incremental learning from noise-free and from noisy data. As usual, we distinguish between learning from positive data and learning from positive and negative data, synonymously called learning from text and learning from informant. Our study relies on the notion of noisy data introduced by Stephan. The basic scenario, named iterative learning, is as fo...

We consider the problem of 1 identifying product features and opinion words in a unified process from Chinese customer reviews when only a much small seed set of opinion words is available. In particular, we consider a problem setting motivated by the task of identifying product features with opinion words and learning opinion words through features alternately and iteratively. In customer revi...

This thesis represents an in depth investigation into the issues raised by the iterative nature of the data-mining process and, in particular, the use of the XCS Learning Classifier System with continuousvalued data-mining problems. The XCS Learning Classifier System has been shown to have the capability for data-mining through rule induction, that is, a technique by which various characteristi...

A test of the predicted interaction between within-category discontinuity and verbal rule complexity on information-integration and rule-based category learning was conducted. Within-category discontinuity adversely affected information-integration category learning but not rule-based category learning. Model-based analyses suggested that some information-integration participants improved perfo...

A new stopping rule is proposed for linear, iterative signal restoration using the gradient descent and conjugate gradient algorithms. The stopping rule attempts to minimize MSE under the assumption that the signal arises from a white noise process. This assumption is appropriate for many coherent imaging applications. The stopping rule is trivial to compute, and for xed relaxation parameters, ...

The paper provides a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every in nite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner takes as input one element of a positive presentation and its previously made hypothesis at a time, and outputs a new hypothesis about the target conce...