We investigate Learning Classifier Systems for online environments that consist of real-valued states and which require every action made by the agent to count towards its performance. Two Learning Classifier System architectures are considered, ZCS and XCS. We use an interval representation with these Learning Classifier Systems for the rule conditions together with roulette wheel action selec...

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good perfor...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three l1, l2, and l3 and could bedetected through screening of blood and bone marrow smears by pathologists. due to being time‑consuming and tediousness of theprocedure, a computer‑based system is acquired for convenient detection of acute lymphoblastic leukemia. microscopic images areacquired fro...

Various methods exist for reducing correlation between classifiers in a multiple classifier framework. The expectation is that the composite classifier will exhibit improved performance and/or be simpler to automate compared with a single classifier. In this paper we investigate how generalisation is affected by varying complexity of unstable base classifiers, implemented as identical single hi...

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over 6 major structurally different base classifiers (enhanced Naïve Bayes, cosine, Bayes Ratio, decision lists, transformationbased learning and maximum variance boost...

In this paper we propose a piece-wise linear classifier for use as the decision stage in a two-modal verification system, comprised of a face and a speech expert. The classifier utilizes a fixed decision boundary that has been specifically designed to account for the effects of noisy audio conditions. Experimental results show that in clean conditions the proposed classifier is outperformed by ...

Recent results in solving classification problems indicate that the use of ensembles classifier models often leads to improved performance over using single classifier models [1, 2, 3, 4]. In this talk, we discuss heterogeneous ensemble classifier models, where the member classifier models are not of the same model type. A discussion of the issues associated with creating such classifiers along...

Characteristics of any classifier heavily depend upon the nature of data set taken for training and verification. Area of app lications like health care suffered from having the large and suitable dataset. Classifier designed for health care should show a better generalization and robustness characteristics so that end results presented by classifier can consider with high reliability and confi...

We investigate Learning Classifier Systems for online environments that consist of real-valued states and which require every action made by the agent to count towards its performance. Two Learning Classifier System architectures are considered, ZCS and XCS. We use an interval representation with these Learning Classifier Systems for the rule conditions together with roulette wheel action selec...