Uplift modeling and Heterogeneous Treatment Effect (HTE) estimation aim at predicting the causal effect of an action, such as a medical treatment or a marketing campaign on a specific individual. In this paper, we focus on data from Randomized Controlled Experiments which guarantee causal interpretation of the outcomes. Class and treatment imbalance are important problems in uplift modeling/HTE, but classical undersampling or oversampling based approaches are hard to apply in this case since they distort the predicted effect. Calibration methods have been proposed in the past, however, they do not guarantee correct predictions. In this work, we propose an approach alternative to undersampling, based on flipping the class value of selected records. We show that the proposed approach does not distort the predicted effect and does not require calibration. The method is especially useful for models based on class variable transformation (modified outcome models). We address those models separately, designing a transformation scheme which guarantees correct predictions and addresses also the problem of treatment imbalance which is especially important for those models. Experiments fully confirm our theoretical results. Additionally, we demonstrate that our method is a viable alternative also for standard classification problems.

Objective: Develop a new method for continuous prediction that utilizes a single temporal pattern ending with an event of interest and its multiple instances detected in the temporal data. Methods: Use temporal abstraction to transform time series, instantaneous events, and time intervals into a uniform representation using symbolic time intervals (STIs). Introduce a new approach to event prediction using a single time intervals-related pattern (TIRP), which can learn models to predict whether and when an event of interest will occur, based on multiple instances of a pattern that end with the event. Results: The proposed methods achieved an average improvement of 5% AUROC over LSTM-FCN, the best-performed baseline model, out of the evaluated baseline models (RawXGB, Resnet, LSTM-FCN, and ROCKET) that were applied to real-life datasets. Conclusion: The proposed methods for predicting events continuously have the potential to be used in a wide range of real-world and real-time applications in diverse domains with heterogeneous multivariate temporal data. For example, it could be used to predict panic attacks early using wearable devices or to predict complications early in intensive care unit patients.

We address the problem of regularization of linear regression models in uplift modeling and heterogeneous treatment effect estimation. We consider interaction models which are commonly used by statisticians in medicine and social sciences to estimate the causal effect of a treatment, and introduce a new type of such a model. We demonstrate the equivalence of all interaction models when no regularization is present, and that this is no longer the case when the model is regularized. Interaction terms introduce implicit correlations between treatment and control coefficients into the regularizer, a fact which has not been previously noted. The correlations depend on the type of interaction model, and by interpreting the regularizer as a prior distribution we were able to pinpoint cases when a given regularized interaction model is most appropriate. An interesting property of the proposed new interaction type is that it allows for smooth interpolation between two types of uplift regression models: the double model and the transformed target model. Our results are valid for both ridge ($L_2$) and Lasso ($L_1$) regularization. Experiments on synthetic data fully confirm our analyses. We also compare the usefulness of various regularization schemes on real data.

In many daily applications, such as meteorology or patient data, the starting and ending times of the events are stored in a database, resulting in time interval data. Discovering patterns from time interval data can reveal informative patterns, in which the time intervals are related by temporal relations, such as before or overlaps. When multiple temporal variables are sampled in a variety of forms, and frequencies, as well as irregular events that may or may not have a duration, time intervals patterns can be a powerful way to discover temporal knowledge, since these temporal variables can be transformed into a uniform format of time intervals. Predicting the completion of such patterns can be used when the pattern ends with an event of interest, such as the recovery of a patient, or an undesirable event, such as a medical complication. In recent years, an increasing number of studies have been published on time intervals-related patterns (TIRPs), their discovery, and their use as features for classification. However, as far as we know, no study has investigated the prediction of the completion of a TIRP. The main challenge in performing such a completion prediction occurs when the time intervals are coinciding and not finished yet which introduces uncertainty in the evolving temporal relations, and thus on the TIRP’s evolution process. To overcome this challenge, we propose a new structure to represent the TIRP’s evolution process and calculate the TIRP’s completion probabilities over time. We introduce two continuous prediction models (CPMs), segmented continuous prediction model (SCPM), and fully continuous prediction model (FCPM) to estimate the TIRP’s completion probability. With the SCPM, the TIRP’s completion probability changes only at the TIRP’s time intervals’ starting or ending point. The FCPM incorporates, in addition, the duration between the TIRP’s time intervals’ starting and ending time points. A rigorous evaluation of four real-life medical and non-medical datasets was performed. The FCPM outperformed the SCPM and the baseline models (random forest, artificial neural network, and recurrent neural network) for all datasets. However, there is a trade-off between the prediction performance and their earliness since the new TIRP’s time intervals’ starting and ending time points are revealed over time, which increases the CPM’s prediction performance.

We present an approach to efficiently construct stepwise regression models in a very high dimensional setting using a multidimensional index. The approach is based on an observation that the collections of available predictor variables often remain relatively stable and many models are built based on the same predictors. Example scenarios include data warehouses against which multiple ad hoc analytical models are built or collections of publicly available open data which remain relatively fixed and are used as a source of predictor variables for many models. We propose an approach where the user simply provides a target variable and the algorithm uses a pre-built multidimensional index to automatically select predictors from millions of available variables, yielding results identical to standard stepwise regression, but an order of magnitude faster. The algorithm has been tested on the large statistical database available from Eurostat, and has been demonstrated to produce interpretable and accurate models. We demonstrate experimentally that our approach produces results that are significantly better than other approaches to modeling with ultra-high dimensional data. Finally, we discuss potential pitfalls such as the presence of highly correlated variables, and show how they can be overcome.

Uplift modeling is an approach to machine learning which allows for predicting the net effect of an action (with respect to not taking the action). To achieve this, the training population is divided into two parts: the treatment group, which is subjected to the action, and the control group, on which the action is not taken. Our task is to construct a model which will predict the difference between outcomes in the treatment and control groups conditional on individual objects' features. When the group assignment is random, the model admits a causal interpretation. When we assume linear responses in both groups, the simplest way of estimating the net effect of the action on an individual is to build two separate linear ordinary least squares (OLS) regressions on the treatment and control groups and compute the difference between their predictions. In classical linear models improvements in accuracy can be achieved through the use of so called shrinkage estimators such as the well known James-Stein estimator, which has a provably lower mean squared error than the OLS estimator. In this paper we investigate the use of shrinkage estimators in the uplift modeling problem. Unfortunately direct generalization of the James-Stein estimator does not lead to improved predictions, nor does shrinking treatment and control models separately. Therefore, we propose a new uplift shrinkage method where estimators in the treatment and control groups are shrunk jointly so as to minimize the error in the predicted net effect of the action. We prove that the proposed estimator does indeed improve on the double regression estimator.

Context: Better methods of evaluating process performance of OSS projects can benefit decision makers who consider adoption of OSS software in a company. This article studies the closure of issues (bugs and features) in GitHub projects, which is an important measure of OSS development process performance and quality of support that project users receive from the developer team. Objective: The goal of this article is a better understanding of the factors that affect issue closure rates in OSS projects. Methodology: The GHTorrent repository is used to select a large sample of mature, active OSS projects. Using survival analysis, we calculate short-term, and long-term issue closure rates. We formulate several hypotheses regarding the impact of OSS project and team characteristics, such as measures of work centralization, measures that reflect internal project workflows, and developer social networks measures on issue closure rates. Based on the proposed features and several control features, a model is built that can predict issue closure rate. The model allows to test our hypotheses. Results: We find that large teams that have many project members have lower issue closure rates than smaller teams. Similarly, increased work centralization increases issue closure rates. While desirable social network characteristics have a positive impact on the amount of commits in a project, they do not have significant influence on issue closure. Conclusion: Overall, findings from empirical analysis support the classic notion of Brook’s – the “surgical team” – in the context of OSS project development process performance on GitHub. The model of issue closure rates proposed in this article is a first step towards an improved understanding and prediction of this important measure of OSS development process performance.

Uplift modeling is a branch of machine learning which aims to predict not the class itself, but the difference between the class variable behavior in two groups: treatment and control. Objects in the treatment group have been subjected to some action, while objects in the control group have not. By including the control group, it is possible to build a model which predicts the causal effect of the action for a given individual. In this paper, we present a variant of support vector machines designed specifically for uplift modeling. The SVM optimization task has been reformulated to explicitly model the difference in class behavior between two datasets. The model predicts whether a given object will have a positive, neutral or negative response to a given action, and by tuning a parameter of the model the analyst is able to influence the relative proportion of neutral predictions and thus the conservativeness of the model. Further, we extend \$\$L_p\$\$Lp-SVMs to the case of uplift modeling and demonstrate that they allow for a more stable selection of the size of negative, neutral and positive groups. Finally, we present quadratic and convex optimization methods for efficiently solving the two proposed optimization tasks.

The Wikipedia project has created one of the largest and best-known open knowledge communities. This community is a model for several similar efforts, both public and commercial, and even for the knowledge economy of the future e-society. For these reasons, issues of quality, social processes, and motivation within the Wikipedia knowledge community have attracted attention of researchers. Research has often used Social Network Analysis applied to networks created based on behavioral data available from the edit history of the Wikipedia. This paper asks the following question: are the popular assumptions about the social interpretations of networks created from the edit history valid? We verify commonly assumed interpretations of four types of networks created from discussions on Wikipedia talk pages, co-edits and reverts in Wikipedia articles, and edits of articles in various topics, by comparing these networks with results from a survey of editors of the Polish Wikipedia community. The results indicate that while the behavioral networks are strongly related to the declarations of respondents, only in one case of the network created from talk pages and interpreted as acquaintance we can observe a near equivalence. The article next describes improved definitions of behavioral indicators obtained through machine learning. The improved networks are much closer to their declarative counterparts. The main contribution of the article is a validated model of an acquaintance network among Wikipedia editors that can be derived from behavioral data and validly interpreted as acquaintance. Other contributions are improved versions of behavioral networks based on editing behavior and discussion history on the Wikipedia.

Uplift modeling is a branch of machine learning which aims at predicting the causal effect of an action such as a marketing campaign or a medical treatment on a given individual by taking into account responses in a treatment group, containing individuals subject to the action, and a control group serving as a background. The resulting model can then be used to select individuals for whom the action will be most profitable. This paper analyzes the use of ensemble methods: bagging and random forests in uplift modeling. We perform an extensive experimental evaluation to demonstrate that the application of those methods often results in spectacular gains in model performance, turning almost useless single models into highly capable uplift ensembles. The gains are much larger than those achieved in case of standard classification. We show that those gains are a result of high ensemble diversity, which in turn is a result of the differences between class probabilities in the treatment and control groups being harder to model than the class probabilities themselves. The feature of uplift modeling which makes it difficult thus also makes it amenable to the application of ensemble methods. As a result, bagging and random forests emerge from our evaluation as key tools in the uplift modeling toolbox.

Uplift modeling is a subfield of machine learning which aims at predicting differences between class probabilities in treatment and control groups. This setting is frequently encountered in medical trials which are thus a natural application domain for the technique. Unfortunately, clinical trials usually involve survival data with censoring and most machine learning methods, including uplift modeling methods, do not directly allow for the use of such data. In this paper we demonstrate that, under reasonable assumptions, uplift modeling can be easily applied to survival data, while maintaining the correctness of decisions made by the model. This is in contrast to standard classification, where the use of censored training examples is more difficult. We test our approach on a publicly available clinical trial dataset and show that using an uplift model it is possible to obtain signifcant improvements in survival rates.

Nowadays Open-Source Software is developed mostly by decentralized teams of developers cooperating on-line. GitHub portal is an online social network that supports development of software by virtual teams of programmers. Since there is no central mechanism that governs the process of team formation, it is interesting to investigate if there are any significant correlations between project quality and the characteristics of the team members. However, for such analysis to be possible, we need good metrics of a project quality. This paper develops two such metrics, first one reflecting project's popularity, and the second one - the quality of support offered by team members to users. The first metric is based on the number of `stars' a project is given by other GitHub members, the second is obtained using survival analysis techniques applied to issues reported on the project by its users. After developing the metrics we have gathered characteristics of several GitHub projects and analyzed their influence on the project quality using statistical regression techniques.

Uplift modeling is a branch of Machine Learning which aims to predict not the class itself, but the difference between the class variable behavior in two groups: treatment and control. Objects in the treatment group have been subject to some action, while objects in the control group have not. By including the control group it is possible to build a model which predicts the causal effect of the action for a given individual. In this paper we present a variant of Support Vector Machines designed specifically for uplift modeling. The SVM optimization task has been reformulated to explicitly model the difference in class behavior between two datasets. The model predicts whether a given object will have a positive, neutral or negative response to a given action, and by tuning a parameter of the model the analyst is able to influence the relative proportion of neutral predictions and thus the sensitivity of the model. We adapt the dual coordinate descent method to efficiently solve our optimization task. Finally the proposed method is compared experimentally with other uplift modeling approaches.

A generalization of the commonly used Maximum Likelihood based learning algorithm for the logistic regression model is considered. It is well known that using the Laplace prior ($L^1$ penalty) on model coefficients leads to a variable selection effect, when most of the coefficients vanish. It is argued that variable selection is not always desirable; it is often better to group correlated variables together and assign equal weights to them. Two new kinds of a-priori distributions over weights are investigated: Gaussian Extremal Mixture (GEM) and Laplacian Extremal Mixture (LEM) which enforce grouping of model coefficients in a manner analogous to $L^1$ and $L^2$ regularization. An efficient learning algorithm is presented, which simultaneously finds model weights and the hyperparameters of those priors. Examples are shown in the experimental part where the proposed a-priori distributions outperform Gauss and Laplace priors as well as other methods which take coefficient grouping into account, such as the elastic net. Theoretical results on parameter shrinkage and sample complexity are also included.

Traditional classification methods predict the class probability distribution conditional on a set of predictor variables. Uplift modeling, in contrast, tries to predict the difference between class probabilities in the treatment group (on which some action has been taken) and the control group (not subjected to the action) such that the model predicts the net effect of the action. Such an approach seems to be well suited to analysis of clinical trial data and to allow for discovering groups of patients for which the treatment is most beneficial. One of the purposes of this paper is to verify this claim experimentally. Additionally, we present an approach to uplift modeling which allows for application of standard probabilistic classification models, such as logistic regression, in the uplift setting. Further, we extend the approach such that standard classification models built on the treatment and control datasets can be incorporated in a manner similar to semi-supervised learning in order to improve prediction accuracy. The usefulness of both approaches has been verified experimentally on publicly available clinical trial data.

Most classification approaches aim at achieving high prediction accuracy on a given dataset. However, in most practical cases, some action, such as mailing an offer or treating a patient, is to be taken on the classified objects and we should model not the class probabilities themselves, but instead, the change in class probabilities caused by the action. The action should then be performed on those objects for which it will be most profitable. This problem is known as uplift modeling, differential response analysis or true lift modeling, but has received very little attention in Machine Learning literature. In the paper we present a tree based classifier tailored specifically to this task. To this end, we design new splitting criteria and pruning methods. The experiments confirm the usefulness of the proposed approach and show significant improvement over previous uplift modeling techniques.

We study a discovery framework in which background knowledge on variables and their relations within a discourse area is available in the form of a graphical model. Starting from an initial, hand-crafted or possibly empty graphical model, the network evolves in an interactive process of discovery. We focus on the central step of this process: given a graphical model and a database, we address the problem of finding the most interesting attribute sets. We formalize the concept of interestingness of attribute sets as the divergence between their behavior as observed in the data, and the behavior that can be explained given the current model. We derive an exact algorithm that finds all attribute sets whose interestingness exceeds a given threshold. We then consider the case of a very large network that renders exact inference unfeasible, and a very large database or data stream. We devise an algorithm that efficiently finds the most interesting attribute sets with prescribed approximation bound and confidence probability, even for very large networks and infinite streams. We study the scalability of the methods in controlled experiments; a case-study sheds light on the practical usefulness of the approach.

The paper presents minimum variance patterns: a new class of itemsets and rules for numerical data, which capture arbitrary continuous relationships between numerical attributes without the need for discretization. The approach is based on finding polynomials over sets of attributes whose variance, in a given dataset, is close to zero. Sets of attributes for which such functions exist are considered interesting. Further, two types of rules are introduced, which help extract understandable relationships from such itemsets. Efficient algorithms for mining minimum variance patterns are presented and verified experimentally.

We address the problem of matching imperfectly documented schemas of data streams and large databases. Instance-level schema matching algorithms identify likely correspondences between attributes by quantifying the similarity of their corresponding values. However, exact calculation of these similarities requires processing of all database records--which is infeasible for data streams. We devise a fast matching algorithm that uses only a small sample of records, and is yet guaranteed to find a matching that is a close approximation of the matching that would be obtained if the entire stream were processed. The method can be applied to any given (combination of) similarity metrics that can be estimated from a sample with bounded error; we apply the algorithm to several metrics. We give a rigorous proof of the method's correctness and report on experiments using large databases.

We address the problem of matching imperfectly documented schemas of data streams and large databases. Instance-level schema matching algorithms identify likely correspondences between attributes by quantifying the similarity of their corresponding values. However, exact calculation of these similarities requires processing of all database records--which is infeasible for data streams. We devise a fast matching algorithm that uses only a small sample of records, and is yet guaranteed to match the most similar attributes with high probability. The method can be applied to any given (combination of) similarity metrics that can be estimated from a sample with bounded error; we apply the algorithm to several metrics. We give a rigorous proof of the method's correctness and report on experiments using large databases.

A new class of associations (polynomial itemsets and polynomial association rules) is presented which allows for discovering nonlinear relationships between numeric attributes without discretization. For binary attributes, proposed associations reduce to classic itemsets and association rules. Many standard association rule mining algorithms can be adapted to finding polynomial itemsets and association rules. We applied polynomial associations to add non-linear terms to logistic regression models. Significant performance improvement was achieved over stepwise methods, traditionally used in statistics, with comparable accuracy.

We examine a new approach to building decision tree by introducing a geometric splitting criterion, based on the properties of a family of metrics on the space of partitions of a finite set. This criterion can be adapted to the characteristics of the data sets and the needs of the users and yields decision trees that have smaller sizes and fewer leaves than the trees built with standard methods and have comparable or better accuracy.

We consider a model in which background knowledge on a given domain of interest is available in terms of a Bayesian network, in addition to a large database. The mining problem is to discover unexpected patterns: our goal is to find the strongest discrepancies between network and database. This problem is intrinsically difficult because it requires inference in a Bayesian network and processing the entire, potentially very large, database. A sampling-based method that we introduce is efficient and yet provably finds the approximatelymost interesting unexpected patterns. We give a rigorous proof of the method's correctness. Experiments shed light on its efficiency and practicality for large-scale Bayesian networks and databases.

In this paper we present an overview of machine learning and data mining methods which we believe are useful for researchers working in the area of speech recognition and analysis. We present selected algorithms for classification, clustering, sequence mining and rough sets based methods. Potential applications in speech recognition and analysis are also discussed. We hope that this paper will encourage speech recognition researchers to more frequently use machine learning and data mining algorithms.

We characterize measures on free Boolean algebras and we examine the relationships that exist between measures and binary tables in relational databases. It is shown that these measures are completely defined by their values on positive conjunctions, and a formula that yields this value is obtained using the method of indicators. An extension of the notion of support that is well suited for tables with missing values is presented. Finally, we obtain Bonferroni-type inequalities that allow for approximative evaluations of these measures for several types of queries. An approximation algorithm and an analysis of the results produced is also included.

Increasing interest in new pattern recognition methods has been motivated by bioinformatics research. The analysis of gene expression data originated from microarrays constitutes an important application area for classification algorithms and illustrates the need for identifying important predictors. We show that the Goodman-Kruskal coefficient can be used for constructing minimal classifiers for tabular data, and we give an algorithm that can construct such classifiers.

The purpose of this paper is to examine the usability of Bonferroni-type combinatorial inequalities to estimation of support of itemsets as well as general Boolean expressions. Families of inequalities for various types of Boolean expressions are presented and evaluated experimentally.

We characterize measures on free Boolean algebras and we examine the relationships that exists between measures and binary tables in relational databases. It is shown that these measures are completely defined by their values on positive conjunctions and an algorithm that leads to the construction of measures starting from its values on positive conjunction is also given, including a formula that allows the evaluation of measures for arbitrary polynomials. Finally, we study pairs of measures generated by ternary tables, that is, by tables that contain missing or unknown values.

The aim of this paper is to present an axiomatization of a generalization of Shannon's entropy starting from partitions of finite sets. The proposed axiomatization yields as special cases the Havrda-Charvat entropy, and thus, provides axiomatizations for the Shannon entropy, the Gini index, and for other types of entropy used in classification and data mining.

We introduce the notion of entropy for a set of attributes of a table in a relational database starting from the notion of entropy for finite functions. We examine the connections that exist between conditional entropies of attribute sets and lossy decompositions of tables and explore the properties of the entropy of sets of attributes regarded as an outer measure on the set of subsets of a heading of a table. Finally, we suggest a generalization of functional dependencies based on conditional entropy.