Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since...

Rough set plays vital role to overcome the complexities, vagueness, uncertainty, imprecision, and incomplete data during features analysis. Classification is tested on certain dataset that maintain an exact class and review process where key attributes decide the class positions. To assess efficient and automated learning, algorithms are used over training datasets. Generally, classification is...

In this paper, first we present A Matlab toolbox which gives the possibility to simulate the data for testing the algorithms such as: Principal Component Analysis (PCA), Factor Analysis(FA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and many other classification methods which can be used in Data Reduction (DR), Data Visualization (DV), supervised and unsupervised ...

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous monoor bivariate classifications most often made by eye. However, a classification must be driven by the data, and so...

The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automatic way, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of a user and the categ...

For very large datasets with more than a few classes, producing ground-truth data can represent a substantial, and potentially expensive, human effort. This is particularly evident when the datasets have been collected for a particular purpose, e.g. scientific inquiry, or by autonomous agents in novel and inaccessible environments. In these situations there is scope for the use of unsupervised ...

The satellite images at different spectral and spatial resolutions with the aid of image processing techniques can improve the quality of information. Especially image fusion is very helpful to extract the spatial information from two images of different resolution images of same area. An operation of image analysis such as image classification on fused images provides better results in compari...

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly “see” more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protectio...

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised boots...

Classifying data is a common task in machine learning. In machine learning, statistical classification is the problem of identifying the sub-population to which new observations belong on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics. In machine learning,...