Tomasz Zabkowski

Model Improvement by the Statistical Decomposition

In this paper we propose applying multidimensional decompositions for modeling improvement. Results generated by different models usually include both wanted and destructive components. Many of the components are common to all the models. Our aim is to find the basis variables with the positive and the negative influence on the modeling task. It will be perofrmed with multidimensional transforamtions such as ICA and PCA.

Noise detection for ensemble methods

In this paper we present a novel noisy signal identification method applied in ensemble methods for destructive components classification. Typically two main signal properties like variability and predictability are described by the same second order statistic characteristic. In our approach we postulate to separate measure of the signal internal dependencies and their variability. The validity of the approach is confirmed by the experiment with energy load data.

The noise identification method based on divergence analysis in ensemble methods context

In this paper we propose a divergence based method for noise detection in ensemble method context where the prediction results from different models are treated as a multidimensional variable that contains constructive and destructive latent components. The crucial stage is the proper destructive and constructive components classification. We propose to calculate the noisiness of the particular latent component as the divergence from chosen reference noise. It allows us to identify the wide range of noises besides the typical signals with close analytical form such as Gaussian or uniform. The real data experiment with load energy prediction confirms presented methodology.

RFM approach for telecom insolvency modeling

Purpose – The purpose of this paper is to present application of recency, frequency and monetary value (RFM) approach to predict customer insolvency using telecommunication data corresponding to RFM of late payments. The study tackles a serious problem that telecommunication companies often face and shows the ways to deal with it. Design/methodology/approach – Based on a real telecom customer data, RFM approach was tested against decision trees and logistic regression models. Proposed models were evaluated with lift measure, area under the receiver operating characteristic and the ability to detect significant amount of money owed by insolvent customers. Findings – The main findings from the research are twofold: RFM approach offers a viable alternative for customer insolvency classification. The proposed models perform well and all of them can capture significant amount of money owed by insolvent customers what is of high importance for the revenue assurance. Originality/value – In comparison to previous...

Comparison of decision trees with Rényi and Tsallis entropy applied for imbalanced churn dataset

Two algorithms for building classification trees, based on Tsallis and Rényi entropy, are proposed and applied to customer churn problem. The dataset for modeling represents highly unbalanced proportion of two classes, which is often found in real world applications, and may cause negative effects on classification performance of the algorithms. The quality measures for obtained trees are compared for different values of α parameter.

Blind Signal Separation Methods for Integration of Neural Networks Results

In this paper it is proposed to apply blind signal separation methods to improve a neural network prediction. Results generated by any regression model usually include both constructive and destructive components. In case of a few models, some of the components can be common to all of them. Our aim is to find the basis elements and distinguish the components with the constructive influence on the modelling quality from the destructive ones. After rejecting the destructive elements from the models results it is observed the enhancement of the results in terms of some standard error criteria. The validity and high performance of the concept is presented on the real problem of energy load prediction

Electricity peak demand classification with artificial neural networks

Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires precision in prediction of peaks in advance. In this paper, our contribution is the proposal of data mining scheme to detect the peak load in the electricity system at country level. For this purpose we undertake the approach different from time series forecasting and represent it as pattern recognition problem. We utilize set of artificial neural networks to benefit from accurate detection of the peaks in the Polish power system. The key finding is that the algorithms can accurately detect 96.2% of the electricity peaks up to 24 hours ahead.

Grade analysis for energy usage patterns segmentation based on smart meter data

The Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to smart meter data on individual household level. The main task of this analysis is to reveal the latent structure of electricity usage patterns and to propose a two dimensional segmentation taking into account the usage of selected home appliances and time of their usage. This provides the solutions applicable in smart metering systems that can support usage forecasting and contribute to higher energy awareness.