The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phase and parameters modified phase. Firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training...
In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic fuzzy clustering of the tissue samples. In this regard, coordinate of the cluster centers have been encoded in the chromosomes and three fuzzy cluster validity indices are simultaneously optimized. Each solution of the resultant Pareto-optimal set has been boosted by a novel technique...
The decision trees and their variants recently have been proposed. All trees used are fixed M-ary tree-structured, such that the training samples in each node must be artificially divided into a fixed number of branches. This study proposes a fuzzy variable-branch decision tree (FVBDT) based on the fuzzy genetic algorithm (FGA). The FGA automatically searches for the proper number of branches of each...
A kernel fuzzy classifier with KFCMC and GA is proposed in this paper. For such classifier, firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. Then in the feature space, training samples are divided into some clusters by proposed KFCMC algorithm. For each created cluster, a fuzzy rule is defined. Some parameters of fuzzy classifier...
This paper introduces a fuzzy inference system, based on the Takagi-Sugeno-Kang model, to achieve efficient and reliable classification in the domain of ubiquitous computing, and in particular for smart or context-aware, sensor-augmented devices. As these are typically deployed in unpredictable environments and have a large amount of correlated sensor data, we propose to use a Gath-Geva clustering...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.