1,721,054 research outputs found
Semi-supervised regression trees with application to QSAR modelling
Full text linkDespite the ease of collecting abundance of data about various phenomena, obtaining labeled data needed for learning models with high predictive performance remains a difficult and expensive task in many domains. This issue is particularly present in the case of the analysis of scientific data where obtaining labeled data typically requires expensive experiments. Moreover, in the analysis of scientific data, another issue is of fundamental importance: the interpretability of the models and the explainability of their decisions. By taking into account these considerations, we propose a novel semi-supervised method to learn regression trees. Thanks to the semi-supervised machine learning approach, the method is able to exploit information coming not only from labeled data, but also from unlabeled data, thus alleviating the issue of lack of labeled data. The method is based on the predictive clustering trees paradigm that extends regression trees towards structured output prediction. This allows us to obtain interpretable regression trees. The method we propose is particularly suited for the chemoinformatics task of quantitative structure-activity relationship (QSAR) modeling, which is the main application context considered in this paper. Specifically, we evaluate the proposed method on 4 QSAR modelling datasets and illustrate its use on a case study of predicting farnesyltransferase inhibitors. Additionally, we also evaluate our approach on 10 benchmark datasets not related to the QSAR modeling problem. The evaluation reveals the following: semi-supervised trees and ensembles thereof have better predictive performance than their supervised counterparts (especially when the number of labeled examples is very small); different datasets and different amounts of labeled data require different amounts of unlabeled data to be included in the learning process; and the learned semi-supervised regression trees can be used to better understand the problem at hand and the way predictions are being made
Dealing with Spatial Autocorrelation when Learning Predictive Clustering Trees
No full textSpatial autocorrelation is the correlation among data values which is strictly due to the relative spatial proximity of the objects that the data refer to. Inappropriate treatment of data with spatial dependencies, where spatial autocorrelation is ignored, can obfuscate important insights. In this paper, we propose a data mining method that explicitly considers spatial autocorrelation in the values of the response (target) variable when learning predictive clustering models. The method is based on the concept of predictive clustering trees (PCTs), according to which hierarchies of clusters of similar data are identified and a predictive model is associated to each cluster. In particular, our approach is able to learn predictive models for both a continuous response (regression task) and a discrete response (classification task). We evaluate our approach on several real world problems of spatial regression and spatial classification. The consideration of the autocorrelation in the models improves predictions that are consistently clustered in space and that clusters try to preserve the spatial arrangement of the data, at the same time providing a multi-level insight into the spatial autocorrelation phenomenon. The evaluation of SCLUS in several ecological domains (e.g. predicting outcrossing rates within a conventional field due to the surrounding genetically modified fields, as well as predicting pollen dispersal rates from two lines of plants) confirms itscapability of building spatial aware models which capture the spatial distribution of the target variable. In general, the maps obtained by using SCLUS do not require further post-smoothing of the results if we want to use them in practice
Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
Full text linkThe reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart
Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification
No full textSemi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received much attention from the research community, this is not the case for complex prediction tasks with structurally dependent variables, such as multi-label classification and hierarchical multi-label classification. These tasks may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of simultaneously predicting multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability of classical tree-based models
Estimating the Importance of Relational Features by Using Gradient Boosting
No full textWith data becoming more and more complex, the standard tabular data format often does not suffice to represent datasets. Richer representations, such as relational ones, are needed. However, a relational representation opens a much larger space of possible descriptors (features) of the examples that are to be classified. Consequently, it is important to assess which features are relevant (and to what extent) for predicting the target. In this work, we propose a novel relational feature ranking method that is based on our novel version of gradient-boosted relational trees and extends the Genie3 score towards relational data. By running the algorithm on six well-known benchmark problems, we show that it yields meaningful feature rankings, provided that the underlying classifier can learn the target concept successfully
Network Regression with Predictive Clustering Trees
No full textRegression inference in network data is a challenging task in machine learning and data mining. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the values of the response variable at a given node depend on the values of the variables (predictor and response) at the nodes connected to the given node. This phenomenon is a direct violation of the assumption of independent (i.i.d.) observations: At the same time, it offers a unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this paper, we propose a data mining method that explicitly considers autocorrelation when building regression models from network data. The method is based on the concept of predictive clustering trees (PCTs), which can be used both for clustering and predictive tasks: PCTs are decision trees viewed as hierarchies of clusters and provide symbolic descriptions of the clusters. In addition, PCTs can be used for multi-objective prediction problems, including multi-target regression and multi-target classification. Empirical results on real world problems of network regression show that the proposed extension of PCTs performs better than traditional decision tree induction when autocorrelation is present in the data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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