Novelty Detection Review State of Art and Discussion of New Innovations in The Main Application Domains

Ouafae, Baida; Oumaima, Louzar; Mariam, Ramdi; Lyhyaoui, Abdelouahid

doi:10.1109/iraset48871.2020.9092147

Cited by 8 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Novelty detection in the literature has been implemented in a variety of ways [38], such as contextualizing novelty in machine learning as information retrieval [39,40], distant combinations of ideas via citation relations [41], first-pass combinations of concepts never before connected [42], knowledge-graphs of concepts within social networks [26], and agent-based simulations of social and individual learning [27].…”

Section: B Novelty Innovation and Redundancymentioning

confidence: 99%

Don't follow the leader: Independent thinkers create scientific innovation

Sean¹,

Baten²,

Proma³

et al. 2023

Preprint

View full text Add to dashboard Cite

Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these "superstars" foster leadership in scientific innovation? We introduce three information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the scholarly output of scientists who are either not connected or strongly connected to superstar scientists. We find that while connected scientists do indeed publish more, garner more citations, and produce more diverse content, this comes at a cost of lower innovation and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these connected scientists diminishes. In contrast, authors that produce innovative content without the benefit of collaborations with scientific superstars produce papers that connect a greater diversity of concepts, publish more, and have comparable citation rates, once one controls for transferred prestige of superstars. On balance, our results indicate that academia pays a price by focusing attention and resources on superstars.

show abstract

Section: B Novelty Innovation and Redundancymentioning

confidence: 99%

Don't follow the leader: Independent thinkers create scientific innovation

Sean¹,

Baten²,

Proma³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Although novelty detection (Ouafae et al 2020) and incremental or continual learning have been studied widely (Chen and Liu 2018;Parisi et al 2019), little work has been done to build a SiOWL system. Here we describe a dialogue system (called CML) that performs a simple SiOWL function continually by itself after the system has been deployed (Mazumder et al 2020;Liu and Mazumder 2021).…”

Section: An Example Siowl Systemmentioning

confidence: 99%

“…Although novelty detection and continual learning have been researched extensively (Ouafae et al 2020), they remain to be challenging. Limited work has been done to address the following (this list is by no means exhaustive):…”

Section: Key Challengesmentioning

confidence: 99%

Self-Initiated Open World Learning for Autonomous AI Agents

Liu¹,

Robertson²,

Grigsby³

et al. 2021

Preprint

View full text Add to dashboard Cite

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and selfsupervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data. As the real-world is an open environment with unknowns or novelties, detecting novelties or unknowns, gathering ground-truth training data, and incrementally learning the unknowns make the agent more and more knowledgeable and powerful over time. The key challenge is how to automate the process so that it is carried out on the agent's own initiative and through its own interactions with humans and the environment. Since an AI agent usually has a performance task, characterizing each novelty becomes necessary so that the agent can formulate an appropriate response to adapt its behavior to cope with the novelty and to learn from it to improve its future responses and task performance. This paper proposes a theoretic framework for this learning paradigm to promote the research of building self-initiated open world learning agents.

show abstract

“…However, both methods either generate new data points to get more data points for the minority class or remove the data points from the majority class to reduce the majority class size, which changes the original data. Novelty detection (ND), also called one-class classification [60], deals with imbalanced data in a different way since knowledge of only one class (minority or majority class) is used during the training phase [38]. ND has some advantages: (1) it is unsupervised; (2) only one class (normal class or given class) data are needed to train models.…”

Section: Introductionmentioning

confidence: 99%

A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Liu

Cao²,

Liu³

et al. 2022

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Accurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.

show abstract

Novelty Detection Review State of Art and Discussion of New Innovations in The Main Application Domains

Cited by 8 publications

References 29 publications

Don't follow the leader: Independent thinkers create scientific innovation

Don't follow the leader: Independent thinkers create scientific innovation

Self-Initiated Open World Learning for Autonomous AI Agents

A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Contact Info

Product

Resources

About