For machine learning (ML) models to be trusted in decision-making, eXplainable AI (XAI) becomes essential. A promising route to XAI is the development of interpretable ML models, e.g. Decision Sets (DS), which apply a swarm of rules for intelligent problem resolution. Although there has been progress in creating explainable DS using Evolutionary Computation (EC), these approaches often struggle with complex problems involving continuous features. This difficulty arises because the stochastic nature of these approaches cannot adapt to the complexity of problems based on continuous features. In response, we propose a groundbreaking method that formulates DS by breaking down the problem into sub-tasks, each addressed by a specific rule, through a novel interpretable framework. By leveraging a divide & conquer tactic and utilizing the inherent distribution of data to direct the EC search, this method assembles a rule-based intelligent model. Through testing on a wide array of datasets, our approach has proven to generate explainable DSs that maintain high prediction accuracy when dealing with complex issues involving continuous features.

Over the past four decades, Learning Classifier Systems (LCSs) have faced challenges in producing accurate and interpretable models for domains with continuous features, mainly due to the irrelevance issue caused by genotypic methods. These methods directly modify genotypes (conditions), leading to the creation of irrelevant rules. Phenotypic LCSs, which first modify a rule's phenotype (covered instance set) before altering its genotype, can avoid this issue. However, previous phenotypic LCSs struggle with overfitting, resulting in lower testing performance. In response, we propose a novel phenotypic LCS featuring innovations: 1) a heterogeneous phenotype approach in the rule discovery mechanism to alleviate overfitting, and 2) Informed Mutation leverages the inherent neighbouring of similar instances to enhance rule generalization, thereby improving model interpretability. The proposed LCS demonstrates its success with superior testing performance and more interpretable models in all experiments compared to other LCSs. Notably, in a problem with 2048 features, the proposed LCS model outperformed the genotypic UCS by achieving a 97.4% testing accuracy with just 13 rules, compared to the UCS's 9961 rules but only 49.9% accuracy.

Hash representation learning of multi-view heterogeneous data is the key to improving the accuracy of multimedia retrieval. However, existing methods utilize local similarity and fall short of deeply fusing the multi-view features, resulting in poor retrieval accuracy. Current methods only use local similarity to train their model. These methods ignore global similarity. Furthermore, most recent works fuse the multi-view features via a weighted sum or concatenation. We contend that these fusion methods are insufficient for capturing the interaction between various views. We present a novel Central Similarity Multi-View Hashing (CSMVH) method to address the mentioned problems. Central similarity learning is used for solving the local similarity problem, which can utilize the global similarity between the hash center and samples. We present copious empirical data demonstrating the superiority of gate-based fusion over conventional approaches. On the MS COCO and NUS-WIDE, the proposed CSMVH performs better than the state-of-the-art methods by a large margin (up to 11.41% mean Average Precision (mAP) improvement).

The multi-view hash method converts heterogeneous data from multiple views into binary hash codes, which is one of the critical technologies in multimedia retrieval. However, the current methods mainly explore the complementarity among multiple views while lacking confidence in learning and fusion. Moreover, in practical application scenarios, the single-view data contains redundant noise. To conduct confidence learning and eliminate unnecessary noise, we propose a novel Adaptive Confidence Multi-View Hashing (ACMVH) method. First, a confidence network is developed to extract useful information from various single-view features and remove noise information. Furthermore, an adaptive confidence multi-view network is employed to measure the confidence of each view and then fuse multi-view features through a weighted summation. Lastly, a dilation network is designed to further enhance the feature representation of the fused features. To the best of our knowledge, we pioneer the application of confidence learning into the field of multimedia retrieval. Extensive experiments on two public datasets show that the proposed ACMVH performs better than state-of-the-art methods (maximum increase of 3.24%). The source code is available at https://github.com/HackerHyper/ACMVH.

Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and inference speed. We validated the effectiveness of DMMH on three public datasets and achieved state-of-the-art results.

The multi-view hash method is crucial in multimedia retrieval via transforming heterogeneous data from multiple views into binary hash codes. Existing methods primarily focus on leveraging complementary information across multiple views, while ignoring the issue of imbalanced optimization. That is, the features from some views in multi-view data are stronger than others, which leads to a less optimization of the networks handling those weaker features. To fully utilize the data from all the views, we propose a novel Adaptive Loss-aware Modulation (ALM) method to address this imbalance issue during the fusion of multi-view features. Specifically, in training, ALM automatically calculates the total loss for each view to reflect the performance of the respective view's backbone network. The modulation coefficient is then determined based on the total loss of the corresponding view. By multiplying the gradient of the network of each view with its corresponding modulation coefficient, we can suppress the gradient update rate of the view with stronger features, while maintaining the normal gradient update rate for the ones with weaker features. Based on ALM, we further introduce a new Balanced Multi-View Hashing (BMVH) method. Extensive experiments on three public datasets demonstrate that the proposed BMVH outperforms state-of-the-art methods, with a maximum increase of 3.22% in mAP.

Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement).

Learning Classifier Systems (LCSs), a series of rules-based evolutionary computation techniques, which have solved a wide range of discrete-feature-based applications over their 40 years of history. Yet, adapting LCSs to complicated continuous-feature-based domains is still an unsolved challenge. This paper proposes newLCS methods specialized for continuous problems. Concretely, phenotype-orientated Absumption, Subsumption, and Mutation are proposed and employed to form and revise rules directly in a single iteration according to the target problems' inherent data distribution, allowing rules to be released from the burden of directly carrying the information of previous instances. Furthermore, a novel representation format supporting fine-grained generalization degree modification is also proposed. Experiments demonstrate for the first time that LCSs are promising techniques in efficiently producing models with satisfactory prediction performance for complicated continuous problems.