## 1 Introduction  
Machine learning has a wide range of applications in the era of artificial intelligence. Massive data generation and storage facilitate the extraction and transfer of useful knowledge from data, enabling machines to become as smart as humans. Examples of machine learning-based extraordinary technologies include autonomous cars [1], biometric based human identification [2], time series forecasting in different domains [3], security [4], and computer vision [5].  
The neural network uses a mathematical structure to gain and store knowledge, which is relevant to machine learning. Furthermore, neural networks can be used for prediction and classification. The classical approach of training neural networks is to provide labeled data, and the use of training algorithms such as backpropagation [6] and machines' extreme learning [7]. For some applications,data cannot be obtained in advance although it can be provided in chunks to the neural network. The training algorithm then uses these chunks to update the neural network's knowledge. An example of this training algorithm is the online sequential extreme learning machine (OSELM) [8]. Knowledge is not recorded in this approach, unlike one-shot training where data are generally available. However, data are provided partially with respect to dimensionality. Thus, the record or chunk of data does not contain all feature types at a given point in time. For instance, features represent sensor data, while some sensors are inactive all the time. Researchers have then developed the feature-adaptive OSELM (FA-OSELM) that can facilitate knowledge transfer [9] to resolve this issue. However, this approach is subject to knowledge loss, which results from the omission of input that corresponds to the disabled features. Input weights are therefore forgotten. When these inputs become active again, the neural network must wait until new knowledge is gained from future data, but old knowledge gained from such inputs is already lost.  
Classifiers are categorized on the basis of their learning and prediction approaches. Some classifiers are regarded as supervised because they require labeled data [10], others as non-supervised because they do not require labeled data [10]. Some classifiers are regarded as offline because they require offline learning [11], whereas others are classified as online and are based on streamed data [8]. In online classifiers, where data are subject to changes in dimensionality, learning transfer enables old knowledge in the new classifier with a new dimension, yet it does not guarantee knowledge preservation after a series of changes in the dimension because learning transfer is a Markov system.  
This article aims to extend an FA-OSELM classifier with external memory, which would allow old knowledge preservation whenever needed. Old knowledge is represented by neural network weights and is labeled according to the pattern of active features, i.e., infinite-term memory online sequential extreme learning machine (ITM-OSELM). The rest of the article is organized as follows. Sections 2-4 tackle problem formulation, literature review, and methodology, respectively. Section 5 discusses the experimental results and analysis. Section 6 presents the conclusions and recommendations for future works.