Temat: 07.05.TP - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Entropy Based Trees to Support Decision Making for Customer Churn Management
Autorzy:: Gajowniczek, K.
Orłowski, A.
Ząbkowski, T.
Powiązania:: https://bibliotekanauki.pl/articles/1398869.pdf
Data publikacji:: 2016-05
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: In this work we analyze empirically customer churn problem from a physical point of view to provide objective, data driven and significant answers to support decision making process in business application. In particular, we explore different entropy measures applied to decision trees and assess their performance from the business perspective using set of model quality measures often used in business practice. Additionally, the decision trees are compared with logistic regression and two machine learning methods - neural networks and support vector machines.
Źródło:: Acta Physica Polonica A; 2016, 129, 5; 971-979
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Q-Entropy Approach to Selecting High Income Households
Autorzy:: Gajowniczek, K.
Karpio, K.
Łukasiewicz, P.
Orłowski, A.
Ząbkowski, T.
Powiązania:: https://bibliotekanauki.pl/articles/1388367.pdf
Data publikacji:: 2015-03
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: A generalized algorithm for building classification trees, based on Tsallis q-entropy, is proposed and applied to classification of Polish households with respect to their incomes. Data for 2008 are used. Quality measures for obtained trees are compared for different values of q parameter. A method of choosing the optimum tree is elaborated.
Źródło:: Acta Physica Polonica A; 2015, 127, 3A; A-38-A-44
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 3.

Tytuł:: Independent Component Analysis for Ensemble Predictors with Small Number of Models
Autorzy:: Szupiluk, R.
Ząbkowski, T.
Gajowniczek, K.
Powiązania:: https://bibliotekanauki.pl/articles/1388270.pdf
Data publikacji:: 2015-03
Wydawca:: Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:: 05.45.Tp
05.40.Ca
07.05.Kf
07.05.Mh
Opis:: The article presents independent component analysis (ICA) applied to the concept of ensemble predictors. The use of ICA decomposition enables to extract components with particular statistical properties that can be interpreted as destructive or constructive for the prediction. Such process can be treated as noise filtration from multivariate observation data, in which observed data consist prediction results. As a consequence of the ICA multivariate approach, the final results are combination of the primary models, what can be interpreted as aggregation step. The key issue of the presented method is the identification of noise components. For this purpose, a new method for evaluating the randomness of the signals was developed. The experimental results show that presented approach is effective for ensemble prediction taking into account different prediction criteria and even small set of models.
Źródło:: Acta Physica Polonica A; 2015, 127, 3A; A-139-A-144
0587-4246
1898-794X
Pojawia się w:: Acta Physica Polonica A
Dostawca treści:: Biblioteka Nauki

Artykuł

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