The ensemble machine learning methods incorporating bagging, random subspace, random forest, and rotation forest employing decision trees, i.e. Pruned Model Trees, as base learning algorithms were developed in WEKA environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. T...

We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers ar...

GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an iterative algorithm for identifying a linear subspace of Rn from data consisting of partial observations of random vectors from that subspace. This paper examines local convergence properties of GROUSE, under assumptions on the randomness of the observed vectors, the randomness of the subset of elements observed at each iteration, ...

With the widespread application of computer network technology, diverse anonymous cyber crimes begin to appear in the online community. The anonymous nature of online-information distribution makes writeprint identification a critical forensic problem. But the difficulty of the task is the huge number of features in even a moderate-sized available text corpus, which causes the problem of over-t...

The selection of feature subspaces for growing decision trees is a key step in building random forest models. However, the common approach using randomly sampling a few features in the subspace is not suitable for high dimensional data consisting of thousands of features, because such data often contains many features which are uninformative to classification, and the random sampling often does...

In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by random subspace techniques and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Experimental r...

SIPEX-G is a fast converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark counterparts makes it a viable alternative in applications where subspace methods are employed. Blind multiuser detection is one such area, where subspace methods, recently developed by researchers, hav...