Data analysis in cardiovascular diseases is difficult due to large massive of information. All of features are not impressive in the final results. So it is very important to identify more effective features. In this study, the method of feature selection with binary cuckoo optimization algorithm is implemented to reduce property. According to the results, the most appropriate classification fo...

 In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification....

A large package of algorithms for feature ranking and selection has been developed. Infosel++, Information Based Feature Selection C++ Library, is a collection of classes and utilities based on probability estimation that can help developers of machine learning methods in rapid interfacing of feature selection algorithms, aid users in selecting an appropriate algorithm for a given task (embed f...

Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of v...

Feature selection based data mining methods is one of the most important research directions in the fields of machine learning in recent years. This paper presents a review of assorted feature selection methods named filter, wrapper and embedded and multiclass classifiers like support vector machines (SVM), decision tree, averaged perceptron and neural network. Additionally it conveys an assess...

Qomroud water conveyance tunnel (lot 1&2) with the length of 16 kilometers is considered as one of the greatest development and national projects in Iran. Since about 2 kilometers of tunnel pass through alluvium and the rest of the tunnel pass through various types of geological units, and due to the complexity of geological condition and variety of effective criteria, suitable selection of exc...

Parameter selection plays a central role in machine learning. The main idea of parameter selection is to choose a subset of relevant parameters for building robust learning models. There are many potential benefits of parameter selection: facilitating data visualization and data understanding, reducing the measurement and storage requirements, reducing training and utilization times, defying th...

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated w...

We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, model selection amounts to tuning kernel parameters and the slack penalty coefficient C. We begin by reviewing a recently developed probabilistic framework for SVM classification. An extension to the case of SVMs with quadratic slack penalties is given and a simple...