Feature selection in credit risk modeling: an international evidence

This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers. As such, to examine the impact of the feature selection method on classifier performance, we use two Chinese and three other real-world credit scoring datasets. The utilized feature selection methods are the least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression trees (CART), logistic regression (LR), artificial neural network (ANN), and support vector machines (SVM). Empirical findings confirm that LASSO’s feature selection method, followed by robust classifier SVM, demonstrates remarkable improvement and outperforms other competitive classifiers. Moreover, ANN also offers improved accuracy with feature selection methods; LR only can improve classification efficiency through performing feature selection via LASSO. Nonetheless, CART does not provide any indication of improvement in any combination. The proposed credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding of this study has practical value, as to date, there is no consensus about the combination of feature selection method and prediction classifiers

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, which produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile.Comment: AAAI 202

Modulation of IncRNA H19 enhances resveratrol‐inhibited cancer cell proliferation and migration by regulating endoplasmic reticulum stress

The phytoalexin resveratrol exhibits anti-tumour activity in many types of cancer. In this study, we showed that resveratrol suppressed the survival of gastric tumour cells both in vivo and in vitro. Resveratrol promoted apoptosis, autophagy and endoplasmic reticulum (ER) stress in a dosedependent manner. In conclusion, resveratrol inhibited cancer cell survival, while knockdown of lncRNA H19 resulted in increased sensitivity to resveratrol therapy

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.Comment: 21 pages, submitted to TKDD. arXiv admin note: text overlap with arXiv:2209.08044, arXiv:2205.1452

3D-printed membrane microvalves and microdecoder

Abstract(#br)A microfluidic system for multichannel switching and multiphase flow control has potential uses in pneumatic soft robotics and biological sampling systems. At present, the membrane microvalves used in microfluidic systems are mostly constructed using a multilayer bonding process so that the device cannot withstand high pressures. In this paper, we demonstrate a design method and the properties of a bondless membrane microvalve fabricated using a commercial 3D printer. We used a multijet (MJP) 3D printer to print a 100-μm-thick and 6-mm-diameter membrane from a relatively hard material (1700 MPa). The membrane’s high toughness ensures that it does not need negative pressure to reopen. The measured operation frequency was less than 2.5 Hz under a pneumatic pressure of 14.5 kPa...