Fraudulent transaction data tend to have several categorical features with high cardinality. It makes data preprocessing complicated if categories in such features do not have an order or meaningful mapping to numerical values. Even though many encoding techniques exist, their impact on highly imbalanced massive data sets is not thoroughly evaluated. Two transaction datasets with an imbalance lower than 1\% of frauds have been used in our study. Six encoding methods were employed, which belong to either target-agnostic or target-based groups. The experimental procedure has involved the use of several machine-learning techniques, such as ensemble learning, along with both linear and non-linear learning approaches. Our study emphasizes the significance of carefully selecting an appropriate encoding approach for imbalanced datasets and machine learning algorithms. Using target-based encoding techniques can enhance model performance significantly. Among the various encoding methods assessed, the James-Stein and Weight of Evidence (WOE) encoders were the most effective, whereas the CatBoost encoder may not be optimal for imbalanced datasets. Moreover, it is crucial to bear in mind the curse of dimensionality when employing encoding techniques like hashing and One-Hot encoding.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatica, Title: Kriging Predictor for Facial Emotion Recognition Using Numerical Proximities of Human Emotions, Authors: Rasa Karbauskaitė, Leonidas Sakalauskas, Gintautas Dzemyda , Emotion recognition from facial expressions has gained much interest over the last few decades. In the literature, the common approach, used for facial emotion recognition (FER), consists of these steps: image pre-processing, face detection, facial feature extraction, and facial expression classification (recognition). We have developed a method for FER that is absolutely different from this common approach. Our method is based on the dimensional model of emotions as well as on using the kriging predictor of Fractional Brownian Vector Field. The classification problem, related to the recognition of facial emotions, is formulated and solved. The relationship of different emotions is estimated by expert psychologists by putting different emotions as the points on the plane. The goal is to get an estimate of a new picture emotion on the plane by kriging and determine which emotion, identified by psychologists, is the closest one. Seven basic emotions (Joy, Sadness, Surprise, Disgust, Anger, Fear, and Neutral) have been chosen. The accuracy of classification into seven classes has been obtained approximately 50%, if we make a decision on the basis of the closest basic emotion. It has been ascertained that the kriging predictor is suitable for facial emotion recognition in the case of small sets of pictures. More sophisticated classification strategies may increase the accuracy, when grouping of the basic emotions is applied.

Multidimensional scaling (MDS) is a widely used technique for mapping data from a high-dimensional to a lower-dimensional space and for visualizing data. Recently, a new method, known as Geometric MDS, has been developed to minimize the MDS stress function by an iterative procedure, where coordinates of a particular point of the projected space are moved to the new position defined analytically. Such a change in position is easily interpreted geometrically. Moreover, the coordinates of points of the projected space may be recalculated simultaneously, i.e. in parallel, independently of each other. This paper has several objectives. Two implementations of Geometric MDS are suggested and analysed experimentally. The parallel implementation of Geometric MDS is developed for multithreaded multi-core processors. The sequential implementation is optimized for computational speed, enabling it to solve large data problems. It is compared with the SMACOF version of MDS. Python codes for both Geometric MDS and SMACOF are presented to highlight the differences between the two implementations. The comparison was carried out on several aspects: the comparative performance of Geometric MDS and SMACOF depending on the projection dimension, data size and computation time. Geometric MDS usually finds lower stress when the dimensionality of the projected space is smaller.

Multi-criteria decision-making (MCDM) methods aim at dealing with certain limitations of human information processing. However, cognitive biases, which are discrepancies of human behavior from the behavior of perfectly rational agents, might persist even when MCDM methods are used. In this article, we focus on two among the most common biases—framing and loss aversion. We test whether these cognitive biases can influence in a predictable way both the criteria weights elicited using the Analytic Hierarchy Process (AHP) and the final ranking of alternatives obtained with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In a controlled experiment we presented two groups of participants with a multi-criteria problem and found that people make different decisions when presented with different but objectively equivalent descriptions (i.e., frames) of the same criteria. Specifically, the results show that framing and loss aversion influenced the responses of decision makers during pairwise comparisons, which in turn caused the rank reversal of criteria weights across groups and resulted in the choice of a different best alternative. We discuss our findings in light of Prospect Theory and show that the particular framing of criteria can influence the outcomes of MCDM in a predictable way. We outline implications for MCDM methodology and highlight possible debiasing techniques.