Feature distillation and accumulated selection for automated fraudulent publisher classification from user click data of online advertising

PurposeThe problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.Design/methodology/approachTo overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.FindingsEmpirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.Originality/valueThe FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

No Gender in ‘Gender Agreement’: On Declension Classes and Gender in Serbo-Croatian

The present paper argues for a view of gender agreement without either grammatical or natural gender being represented as syntactic features. Rather than deriving declension classes in terms of realisation, I postulate them as the only relevant feature that is lexically specified on the noun. Agreement copies the declension class and triggers presuppositions. When these presuppositions clash with those already active in the discourse, default agreement is realised. The paper moreover provides a quantitative analysis of semantic correlates of declension classes and a novel analysis of SC declension classes.

Rationale Discovery and Explainable AI

The justification of an algorithm’s outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.

A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index

Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira ) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.

Task complexity moderates group synergy

Complexity—defined in terms of the number of components and the nature of the interdependencies between them—is clearly a relevant feature of all tasks that groups perform. Yet the role that task complexity plays in determining group performance remains poorly understood, in part because no clear language exists to express complexity in a way that allows for straightforward comparisons across tasks. Here we avoid this analytical difficulty by identifying a class of tasks for which complexity can be varied systematically while keeping all other elements of the task unchanged. We then test the effects of task complexity in a preregistered two-phase experiment in which 1,200 individuals were evaluated on a series of tasks of varying complexity (phase 1) and then randomly assigned to solve similar tasks either in interacting groups or as independent individuals (phase 2). We find that interacting groups are as fast as the fastest individual and more efficient than the most efficient individual for complex tasks but not for simpler ones. Leveraging our highly granular digital data, we define and precisely measure group process losses and synergistic gains and show that the balance between the two switches signs at intermediate values of task complexity. Finally, we find that interacting groups generate more solutions more rapidly and explore the solution space more broadly than independent problem solvers, finding higher-quality solutions than all but the highest-scoring individuals.

Materials In Paintings (MIP): An interdisciplinary dataset for perception, art history, and computer vision

In this paper, we capture and explore the painterly depictions of materials to enable the study of depiction and perception of materials through the artists’ eye. We annotated a dataset of 19k paintings with 200k+ bounding boxes from which polygon segments were automatically extracted. Each bounding box was assigned a coarse material label (e.g., fabric) and half was also assigned a fine-grained label (e.g., velvety, silky). The dataset in its entirety is available for browsing and downloading at materialsinpaintings.tudelft.nl. We demonstrate the cross-disciplinary utility of our dataset by presenting novel findings across human perception, art history and, computer vision. Our experiments include a demonstration of how painters create convincing depictions using a stylized approach. We further provide an analysis of the spatial and probabilistic distributions of materials depicted in paintings, in which we for example show that strong patterns exists for material presence and location. Furthermore, we demonstrate how paintings could be used to build more robust computer vision classifiers by learning a more perceptually relevant feature representation. Additionally, we demonstrate that training classifiers on paintings could be used to uncover hidden perceptual cues by visualizing the features used by the classifiers. We conclude that our dataset of painterly material depictions is a rich source for gaining insights into the depiction and perception of materials across multiple disciplines and hope that the release of this dataset will drive multidisciplinary research.