Classifier Pool Generation based on a Two-level Diversity Approach

Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation

Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over …

Revisiting Local Descriptor for Improved Few-Shot Classification

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts have been aimed at designing more and more complex classifiers that measure similarities between query and support images, but left the importance of feature embeddings seldom explored. We show that the reliance on sophisticated classifiers is not necessary, and a simple classifier applied directly to improved feature embeddings can instead outperform most of the leading methods in the literature. To this end, we pre…

ExMobileViT：移动视觉Transformer的轻量级分类器扩展

ExMobileViT: Lightweight Classifier Extension for Mobile Vision Transformer

ExMobileViT通过重用早期注意力阶段的平均池化特征扩展分类器通道，以极小开销提升MobileViT精度，在ImageNet上仅增约5%参数即获显著提升。

Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation

We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub-domains. We then train an evaluator model which is used to pick the best caption f…

使用裂纹图像数据集研究深度卷积神经网络中调参参数的影响

Impact of Tuning Parameters in Deep Convolutional Neural Network Using a Crack Image Dataset

本文通过裂纹图像数据集实验研究了池化、激活函数和优化器等调参参数对DCNN分类性能的影响，发现最大池化配合adam优化器和tanh激活函数可获得更优性能。

面向合成数据学习的质量-多样性生成采样

Quality-Diversity Generative Sampling for Learning with Synthetic Data

针对生成模型合成训练数据易传递偏差的问题，提出无需微调生成模型的质量-多样性生成采样框架，通过提示引导在多样性维度优化数据质量，实现数据均衡，提升分类器公平性且保持准确率。

多数据集下的3D神经影像分类多实例学习基准测试

A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification

本文通过三个CT和四个MRI数据集比较了简单MIL、基于注意力的MIL、3D CNN和3D ViTs，发现简单均值池化MIL在四个中等规模任务中表现优异，且训练速度快25倍，揭示了现有MIL方法的局限性。

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets have been created to support such work. However, up until now, less work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this paper, two new publicly available datasets are introduced, the 2-Class Sudanese Sentiment Dataset (SudSenti2) and the …

The Effect of Structural Diversity of an Ensemble of Classifiers on Classification Accuracy

This paper aims to showcase the measure of structural diversity of an ensemble of 9 classifiers and then map a relationship between this structural diversity and accuracy. The structural diversity was induced by having different architectures or structures of the classifiers The Genetical Algorithms (GA) were used to derive the relationship between diversity and the classification accuracy by evolving the classifiers and then picking 9 classifiers out on an ensemble of 60 classifiers. It was found that as the ensemble became diverse the accuracy improved. However at a certain diversity measure…

Online local pool generation for dynamic classifier selection: an extended version

Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifier's competence, the manner in which the pool is generated could affect the choice of the best classifier for a given sample. That is, the global perspective in which pools are generated may not help the DCS techniques in selecting a competent classifier for samples that are likely to be mislabelled. Thus, we propose in this work an online pool generation method that produces…

Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks. Specifically, after learning base classifiers, the algorithm i) increases the weights of positive examples (minority class) which are "hard" to classify with uniformly weighted base classifiers; and ii) then learns weights over base classifiers by optimizing the PAC-Bayesian C-Bound that takes into account the accuracy and diversity between the classifiers. We show efficiency of the proposed approach with respect to state-of-art models on pre…

Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to opti…

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have …

A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces

The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important properties involved in the selection of the best EoC from a pool of classifiers is considered to be classifier diversity. In general, classifier diversity does not occur randomly, but is generated systematically by various ensemble creation methods. By using diverse data subsets to train classifiers, these methods can create diverse classifiers for the EoC. In this work, we propose a scheme to measure data diversity direct…

使用集成分类与证据理论的异常检测

Anomaly Detection using Ensemble Classification and Evidence Theory

本文提出结合集成分类、证据理论与不确定性量化的异常检测方法，通过分类器池选择和多类集成架构提升未知条件识别能力，并在 Tennessee Eastman 基准上验证。

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of cl…

META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach

In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. In the second phase the meta-features are computed using the training data and used to trai…

Land Cover Mapping Using Ensemble Feature Selection Methods

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices. For each ensemble, the classification accuracy was derived as …

Becoming More Robust to Label Noise with Classifier Diversity

It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data sets. In this paper, we present noise identification using classifier diversity (NICD) -- a method for deriving a less biased noise measurement and integrating it into the learning process. To lessen the bias of the noise measure, NICD selects a div…

MLRS-PDS：一种面向动态集成选择流水线的元学习推荐方法

MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines

针对动态集成选择中分类器池不稳定冗余影响效率与精度的问题，提出元学习推荐系统MLRS，基于数据集元特征推荐最优池生成方案与DES方法，在288个数据集上优于传统策略。

Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. This paper also enhances classification accuracy by extracting new frequency domain features. Many researchers have obtained these features from global positioning system data; however, this data was excluded in this paper, as the system use might deplete the smartphone's battery and signals may be lost in some areas. Our proposed two-layer framework differs from previous classification attempts in three distinct ways: 1) the outputs of the two…

Condorcet's Jury Theorem for Consensus Clustering and its Implications for Diversity

Condorcet's Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet's Jury Theorem to the mean partition approach under the additional assumptions that a unique ground-truth partition exists and sample partitions are drawn from a sufficiently small ball containing the ground-truth. As an implication of practical relevance, we question the claim that the quality of consensus cluste…

Ensemble pruning via an integer programming approach with diversity constraints

Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classifiers, can lead to comparable or better predictions than using all classifiers. In this paper, we consider a binary classification problem and propose an integer programming (IP) approach for selecting optimal classifier subsets. We propose a flexible objective function to adapt to desired criteria of different datasets. We also propose constraints to ensure minimum diversity le…

扩散模型中分类器自由引导的分阶段动态

Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models

本文研究了扩散模型中分类器自由引导的分阶段动态，揭示了引导对采样过程三个阶段的影响，并提出时间变化的引导策略以提升质量和多样性。

The use of entropy to measure structural diversity

In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble dominated by classifiers with the same structure produced poor accuracy. Uncertainty rule from information theory was also used to further define diversity. Genetic algorithms were used to find the op…

A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers

Dynamic ensemble selection (DES) techniques work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. Hence, the key issue in DES is the criterion used to estimate the level of competence of the classifiers in predicting the label of a given test sample. In order to perform a more robust ensemble selection, we proposed the META-DES framework using meta-learning, where multiple criteria are encoded as meta-features and are passed down to a meta-classifier that is trained to estimate the co…

Twin Auxiliary Classifiers GAN

Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases, hence limiting its power on large-scale data. In this paper, we identify the source of the low diversity issue theoretically and propose a practical solution to solve the problem. We show that the auxiliary classifier …

On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models

Surgical risk increases significantly when patients present with comorbid conditions. This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making. The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire perioperative period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (UK SORT) by addressing the class imbalance within the dataset. Our proposed …

Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi-class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simulta…

Diversity of Ensembles for Data Stream Classification

When constructing a classifier ensemble, diversity among the base classifiers is one of the important characteristics. Several studies have been made in the context of standard static data, in particular, when analyzing the relationship between a high ensemble predictive performance and the diversity of its components. Besides, ensembles of learning machines have been performed to learn in the presence of concept drift and adapt to it. However, diversity measures have not received much research interest in evolving data streams. Only a few researchers directly consider promoting diversity whil…

多样性与扩散：基于Stable Diffusion的合成图像分布观察

Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion

本文通过分析Stable Diffusion等文本到图像系统生成的合成图像与自然图像的语义差异，揭示出歧义性、提示遵循、多样性不足和概念表示缺失四个限制因素，并提供了关于CLIP嵌入几何的新见解。

HSFN：构建异构集成以进行假新闻检测的分层选择方法

HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble

针对假新闻检测中集成分类器多样性不足的问题，提出HSFN，通过成对多样性、层次聚类和HierarchySelect选择高多样性且性能可靠的异构分类器池。

用于合成数据生成的激活引导：多样性在下游安全检测中的作用

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection

研究激活引导（AS）生成高质量训练数据用于下游安全检测分类器，发现多样性是关键但被忽视的轴，且AS在窄参数范围内优于提示生成。

SemiScope: 解耦半监督安全分类中的分类器调优与联合优化

SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification

针对半监督安全分类中黑盒使用导致性能增益归因不清的问题，提出SemiScope分析工具，通过贝叶斯优化联合调优SSL设置、置信过滤、过采样和分类器，并与仅调优分类器的对照实验对比，发现分类器超参数优化可恢复大部分增益。

难民安置过程中的匹配、预测与反事实危害

Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes

本研究针对难民安置中的算法匹配可能产生反事实危害的问题，提出后处理算法和Transformer模型以修改预测，确保最优匹配策略不有害，实验验证其有效性。

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on real-world datasets and draw useful conclusions about their behavior. These methods include simple aggregation, meta-learning, cluster-based meta-learning, and ensemble selection using heterogeneous classifiers trained on resampled data to im…

同构量子分类器的加权量子集成

A weighted quantum ensemble of homogeneous quantum classifiers

提出一种加权同构量子集成方法，利用带索引寄存器的量子分类器，通过叠加态和受控酉操作实现数据子采样与量子并行执行，并结合经典权重优化，有效提升了预测性能。

Pantypes：自解释模型的多样化代表

Pantypes: Diverse Representatives for Self-Explainable Models

针对原型自解释分类器存在表示偏差、难以覆盖低密度区域的问题，提出pantypes新家族，通过稀疏对象捕获输入分布全多样性，增强模型多样性、可解释性与公平性。

Simultaneous Relevance and Diversity: A New Recommendation Inference Approach

Relevance and diversity are both important to the success of recommender systems, as they help users to discover from a large pool of items a compact set of candidates that are not only interesting but exploratory as well. The challenge is that relevance and diversity usually act as two competing objectives in conventional recommender systems, which necessities the classic trade-off between exploitation and exploration. Traditionally, higher diversity often means sacrifice on relevance and vice versa. We propose a new approach, heterogeneous inference, which extends the general collaborative f…

具有无分类器类吉布斯引导的条件扩散模型

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance

本文指出了无分类器引导（CFG）缺乏定义良好的去噪扩散模型基础，通过引入Rényi散度修正项并提出类吉布斯采样程序，在保持样本多样性的同时显著提升了图像与文本到音频生成的质量。

Majority Vote of Diverse Classifiers for Late Fusion

In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassific…

Informed Sampling for Diversity in Concept-to-Text NLG

Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus …

理解扩散语言模型中的质量-多样性权衡

Understanding the Quality-Diversity Trade-off in Diffusion Language Models

针对扩散语言模型缺乏质量-多样性调控手段的问题，本文提出结合无分类器引导与随机截断，在序列到序列任务中操控并改善该权衡，从而提升模型性能。