Ž . The Fuzzy ARTMAP neural network is a supervised pattern recognition method based on fuzzy adaptive resonance theory ART . It is Ž a promising method since Fuzzy ARTMAP is able to carry out on-line learning without forgetting previously learnt patterns stable . Ž . learning , it can recode previously learnt categories adaptive to changes in the environment and is self-organising. This paper ...

Information extraction identifies useful and relevant text in a document and converts unstructured text into a form that can be loaded into a database table. Named entity extraction is a main task in the process of information extraction and is a classification problem in which words are assigned to one or more semantic classes or to a default non-entity class. A word which can belong to one or...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way mitigate this problem is combine unsupervised component as a front end of the main learner. paper, we propose hierarchical combination fuzzy C-means clustering and max–min neural network learner for purpose. The proposed method evaluated in doma...

In several applications of data mining to high-dimensional data, clustering techniques developed for low-to-moderate sized problems obtain unsatisfactory results. This is an aspect of the curse of dimensionality issue. A traditional approach is based on representing the data in a suitable similarity space instead of the original high-dimensional attribute space. In this paper, we propose a solu...

Quantitative analysis of brain lesions and ischemic infarcts is becoming very important due to their association with cardiovascular disease and normal aging. In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images. The method combines intensity based fuzzy c-means (FCM) segmentation...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

Function al reasoning or the Takagi-Sugeno-Kang model is a fuzzy reasoning method aiming at numerical accuracy and has found wide use in fuzzy modeling. ln this method, each rule consists of a fuzzy implication and a functional consequence part. ln this work, a new, online identification method for such a system is presented, for supervised learning tasks. Structure identification is executed b...

Supervised named-entity recognition (NER) systems perform better on text that is similar to its training data. Despite this, systems are often trained with as much data as possible, ignoring its relevance. This study explores if NER can be improved by excluding out of domain training data. A maximum entropy model is developed and evaluated twice with each domain in Stockholm-Umeå Corpus (SUC), ...