Experiments with two Approaches for Tracking Drifting Concepts

This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the size of the time window, aiming to maximise the classification accuracy on the new examples. Both methods are general in nature and can be used with any learning algorithm. The objectives of the conducted experiments were to compare the mechanisms and explore whether they can be combined to achieve a synergetic e ect. Results from experiments with three basic learning algorithms (kNN, ID3 and NBC) using four datasets are reported and discussed.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Using performance measurement models for benefit realization with enterprise systems: The Queensland Government approach (Case Study)

A range of influences, technical and organizational, has encouraged the wide spread adaption of Enterprise Systems (ES). Nevertheless, there is a growing consensus that Enterprise Systems have in the many cases failed to provide the expected benefits to organizations. This paper presents ongoing research, which analyzes the benefits realization approach of the Queensland Government. This approach applies a modified Balance Scorecard. First, history and background of Queensland Government’s Enterprise Systems initiative is introduced. Second, the most common reasons for ES under performance are related. Third, relevant performance measurement models and the Balanced Scorecard in particular are discussed. Finally, the Queensland Government initiative is evaluated in light of this overview of current work in the area. In the current and future work, the authors aim to use their active involvement in Queensland Government’s benefits realization initiative for an Action Research based project investigating the appropriateness of the Balanced Scorecard for the purposes of Enterprise Systems benefits realization.