A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

Data stream mining is a fast growing research topic due to the ubiquity of data in several real-world problems. Given their ephemeral nature, data stream sources are expected to undergo changes in data distribution, a phenomenon called concept drift. This paper focuses on one specific type of drift that has not yet been thoroughly studied, namely feature drift. Feature drift occurs whenever a subset of features becomes, or ceases to be, relevant to the learning task; thus, learners must detect and adapt to these changes accordingly. We survey existing work on feature drift adaptation with both explicit and implicit approaches. Additionally, we benchmark several algorithms and a naive feature drift detection approach using synthetic and real-world datasets. The results from our experiments indicate the need for future research in this area as even naive approaches produced gains in accuracy while reducing resources usage. Finally, we state current research topics, challenges and future directions for feature drift adaptation

Interpretable machine learning models for the estimation of seismic drifts in CLT buildings

An accurate estimation of drift demands is crucial for designing and assessing structures under seismic loads. Given the novelty of massive timber buildings, predictive models for the estimation of drifts in mid- to high-rise CLT structures are lacking, particularly in the form of simple models suitable for preliminary design evaluations or regional seismic assessments. In this paper, we present and compare several Machine Learning (ML) models for the estimation of peak inter-storey and roof drifts in multi-storey Cross-Laminated Timber (CLT) walled structures. The ML techniques used include: Multiple Linear Regression, Regression Trees, Random Forest, K-nearest Neighbour, and Support Vector Regression. To this end, 69 structures spanning mid-rise to tall timber buildings are subjected to a large collection of acceleration records and used to create the training and testing datasets. Different structural configurations and behaviour factors, related to the assumed energy dissipation capacity of the buildings, are considered. A diversity of feature selection techniques informs our choice of parameters to the reduced input space leading to a set of six most efficient features: the spectral acceleration at the building’s fundamental period (Sa(T1)), the Peak Ground Velocity (PGV), tuning ratio (T1/Tm), behaviour factor (q), wall height (Hw), and the wall subdivision ratio (Wr). After verifying the high accuracy of our model predictions, the SHapley Additive exPlanation method (SHAP) is used to gain insight into the influence of key input features on the ML model outputs. Finally, our ML drift estimations are compared against previous proposals and design code assumptions, and the potential causes of disagreement are discussed

An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

In today's modern world, the usage of technology is unavoidable, and the rapid advances in the Internet and communication fields have resulted in the expansion of wireless sensor network (WSN) technology. However, WSN has been proven to be vulnerable to security breaches. The harsh and unattended deployment of these networks, combined with their constrained resources and the volume of data generated, introduces a major security concern. WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online stream analysis, allowing the identification of attacks and intrusions. Our aim is to develop an intelligent and efficient intrusion detection system by applying an important machine learning concept known as ensemble learning in order to improve detection performance. Although ensemble models have been proven to be useful in offline learning, they have received less attention in streaming applications. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis on a specialized WSN detection system (WSN-DS) dataset in order to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and Scheduling among normal network traffic. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84 % and 97.2 %, respectively. The above models are efficient and effective in dealing with concept drift while taking into account WSN resource constraints

Continual learning from stationary and non-stationary data

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose the first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user\u27s memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors

OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams

How to get insights from relational data streams in a timely manner is a hot research topic. This type of data stream can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While existing studies have been done on incremental learning for data streams, their evaluations are mostly conducted with manually partitioned datasets. Thus, a natural question is how those open environment challenges look like in real-world relational data streams and how existing incremental learning algorithms perform on real datasets. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in relational data streams. Specifically, we investigate 55 real-world relational data streams and establish that open environment scenarios are indeed widespread in real-world datasets, which presents significant challenges for stream learning algorithms. Through benchmarks with existing incremental learning algorithms, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by missing values, distribution shifts, or anomalies in real-world data streams. The current techniques are insufficient in effectively mitigating these challenges posed by open environments. More researches are needed to address real-world open environment challenges. All datasets and code are open-sourced in https://github.com/sjtudyq/OEBench

Towards Cross-Domain Continual Learning

Continual learning is a process that involves training learning agents to sequentially master a stream of tasks or classes without revisiting past data. The challenge lies in leveraging previously acquired knowledge to learn new tasks efficiently, while avoiding catastrophic forgetting. Existing methods primarily focus on single domains, restricting their applicability to specific problems. In this work, we introduce a novel approach called Cross-Domain Continual Learning (CDCL) that addresses the limitations of being limited to single supervised domains. Our method combines inter- and intra-task cross-attention mechanisms within a compact convolutional network. This integration enables the model to maintain alignment with features from previous tasks, thereby delaying the data drift that may occur between tasks, while performing unsupervised cross-domain (UDA) between related domains. By leveraging an intra-task-specific pseudo-labeling method, we ensure accurate input pairs for both labeled and unlabeled samples, enhancing the learning process. To validate our approach, we conduct extensive experiments on public UDA datasets, showcasing its positive performance on cross-domain continual learning challenges. Additionally, our work introduces incremental ideas that contribute to the advancement of this field. We make our code and models available to encourage further exploration and reproduction of our results: \url{https://github.com/Ivsucram/CDCL}Comment: 12 pages, 2 Figures, 4 Tables. To be published at the IEEE International Conference on Data Engineering (ICDE) 202