Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

Learning the attribute selection measures for decision tree

Decision tree has most widely used for classification. However the main influence of decision tree classification performance is attribute selection problem. The paper considers a number of different attribute selection measures and experimentally examines their behavior in classification. The results show that the choice of measure doesn't affect the classification accuracy, but the size of the tree is influenced significantly. The main effect of the new attribute selection measures which base on normal gain and distance is that they generate smaller trees than traditional attribute selection measures. © 2013 SPIE

SUICIDE AND SUICIDE ATTEMPT DESCRIPTORS BY MULTIMETHOD APPROACH

Background: Suicide is a complex action of suicidal methods and peripheral factors with seemingly threatening components representing actual cause for the suicidal actions. It is especially those, apparently unimportant factors that represent a crucial milestone in the network of all the other, personal, cultural, genetic and biochemical factors, forming the method of action consequently deciding between life and death. Subjects and methods: Based on the Register of Suicides in the Republic of Slovenia kept by the University Psychiatric Clinic Ljubljana, we used a combination of attributes varying within a variable and between variables. Due to limited application of standard statistical methods and analyses in such cases, we used the Machine learning method, Multimethod hybrid approach, which allows combining of different approaches to machine learning (decision trees, genetic algorithms and supplementary vectors). The research included 56712 persons attempting suicide and 21913 persons committing suicide. We chose a form of a suicide action with both possible results: attempted suicide and suicide. Results: Based on the analysis of machine learning, we defined attributes of the action regarding their lethal effect: attempted suicide and suicide commitment. The suicide register kept for the last 40 years shows hanging as the most commonly used suicidal method, used by men with the purpose of causing suicidal death rather than a suicidal attempt. On the other hand, use of medicaments is linked to the suicidal attempt and mostly used by females. Conclusions: All methods of suicidal actions cannot predict suicidal death, thus we examined different methods of suicide to most accurately predict the link between the method and its effect in terms of suicide attempt or suicide. The Machine learning method confirmed the attributes of suicide methods in connection with their different outcomes. This analytical method is useful in processing large databases since it enables one variable’s intensity to affect other variables in terms of result and meaning. The identification of the most decisive risk factors for suicidal behaviour can serve as basis for planning an effective prevention strategies, timely identification and adequate proffessional help to the high risk persons

Pairwise learning to rank by neural networks revisited:reconstruction, theoretical analysis and practical performance

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.Comment: 16 pages, 8 figure

Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables

Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests