Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models’ generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, this survey proposes a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities by investigating how to take advantage of the intrinsic relationship between and within instances. Additionally, it categorizes data augmentation methods across five data modalities through a unified inductive approach.

In our previous study, we propose a multi-agent reinforcement learning framework for the feature selection problem. Specifically, we first reformulate feature selection with a reinforcement learning framework by regarding each feature as an agent. Besides, we obtain the state of the environment in three ways, i.e., statistic description, autoencoder, and graph convolutional network (GCN), in order to derive a fixed-length state representation as the input of reinforcement learning. In addition, we study how the coordination among feature agents can be improved by a more effective reward scheme. Also, we provide a GMM-based generative rectified sampling strategy to accelerate the convergence of multi-agent reinforcement learning. Our method searches the feature subset space globally and can be easily adapted to real-time scenarios due to the nature of reinforcement learning. In the extended version, we further accelerate the framework from two aspects. Specifically, from the sampling aspect, we show the indirect acceleration by proposing a rank-based softmax sampling strategy, and from the exploration aspect, we show the direct acceleration by proposing an interactive reinforcement learning (IRL)-based exploration strategy. Extensive experimental results show the significant improvement of the proposed method over conventional approaches.

In this paper, we study knowledge concept recommendation in Massive Open Online Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units (e.g., videos) in MOOCs, refer to topics and skills that students are expected to master. Comparing to traditional course recommendation in MOOCs, knowledge concepts recommendation has drawn more attention because students′ interests over knowledge concepts can better reveal students′ real intention in a more refined granularity. However, there are three unique challenges in knowledge concept recommendation: (1) How to design an appropriate data structure to capture complex relationships between knowledge concepts, course units, and other participants (e.g., students, teachers)? (2) How to model interactions between students and knowledge concepts? (3) How to make explainable recommendation results to students? To tackle these challenges, we formulate the knowledge concept recommendation as a reinforcement learning task integrated with MOOC knowledge graph (KG). Specifically, we first construct MOOC KG as the environment to capture all the relationships and behavioral histories by considering all the entities (e.g., students, teachers, videos, courses and knowledge concepts) on the MOOC provider. Then, to model the interactions between students and knowledge concepts, we train an agent to mimic students′ learning behavioral patterns facing the complex environment. Moreover, to provide explainable recommendation results, we generate recommended knowledge concepts in the format of a path from MOOC KG to indicate semantic reasons. Finally, we conduct extensive experiments on a real-world MOOC dataset to demonstrate the effectiveness of our proposed method.

Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, Kunpeng Liu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

We study the problem of balancing effectiveness and efficiency in automated feature selection. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection (e.g., mRMR) is mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection automatically navigates to search the best subset, but is usually inefficient. Can we bridge the gap between effectiveness and efficiency under automation Motivated by this dilemma, we aim to develop a novel feature space navigation method. In our preliminary work, we leveraged interactive reinforcement learning to accelerate feature selection by external trainer-agent interaction. In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning and decision tree feedback. Our preliminary work can be significantly improved by modeling the structured knowledge of the downstream task (e.g., decision tree) as learning feedback. In particular, the tree-structured feature hierarchy of decision tree is leveraged to improve state representation; the feature importance hierarchy from decision tree is exploited to develop a new reward scheme. In addition, agents historical action records can also be feedback for another new reward scheme. Finally, extensive experiments demonstrate the improved performances of our methods.

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. These provide empirical evidence of the strides made in this journal version over the initial work, solidifying our framework’s standing as a substantial contribution to the field of automated feature transformation. To improve the reproducibility, we have released the associated code and data by the Github link https://github.com/coco11563/TKDD2023_code.