An incremental learning method based on formal concept analysis for pattern recognition in nonstationary sensor-based smart environments

Abstract Smart homes are typical nonstationary environments that keep generating new data. Ideally, predictive models learned from historical data should automatically adapt to new data without retraining. However, for many data mining algorithms, adding new data with new features to an existing model means we need to retrain the entire model. Compared with static ones, the models having incremental learning mechanisms are more suitable for handling streaming data in the aspect of self-adaptation. Therefore, we propose an incremental algorithm based on the formal concept analysis (FCA) for mining sequential patterns and apply it to recognize various human behavioral patterns in nonstationary sensor-based smart homes. It can automatically adapt to new training data with new classes or features to enhance the currently built model and have competitive recognition results compared to other classical graphical models.

[1]  Hongyi Li,et al.  An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors , 2012, IEEE Transactions on Information Technology in Biomedicine.

[2]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[3]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[4]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[5]  Xiaohui Peng,et al.  A novel random forests based class incremental learning method for activity recognition , 2018, Pattern Recognit..

[6]  Albert Bifet,et al.  Adaptive learning and mining for data streams and frequent patterns , 2009, SKDD.

[7]  Mykola Pechenizkiy,et al.  An Overview of Concept Drift Applications , 2016 .

[8]  Bruno Bouchard,et al.  Cognitive Errors Detection: Mining Behavioral Data Stream of People with Cognitive Impairment , 2016, PETRA.

[9]  Gregory Ditzler,et al.  Incremental Learning of Concept Drift from Streaming Imbalanced Data , 2013, IEEE Transactions on Knowledge and Data Engineering.

[10]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[11]  Matthieu Geist,et al.  Human Activity Recognition Using Recurrent Neural Networks , 2017, CD-MAKE.

[12]  A Min Tjoa,et al.  Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI , 2018, CD-MAKE.

[13]  J. Bordat.  Calcul pratique du treillis de Galois d'une correspondance , 1986 .

[14]  Rokia Missaoui,et al.  Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges , 2004, ICFCA.

[15]  Vincenzo Loia,et al.  Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis , 2012, Inf. Process. Manag..

[16]  Heiko Wersing,et al.  KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[17]  Li-Chen Fu,et al.  Hybrid User-Assisted Incremental Model Adaptation for Activity Recognition in a Dynamic Smart-Home Environment , 2013, IEEE Transactions on Human-Machine Systems.

[18]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[19]  Diane J. Cook,et al.  Recognizing independent and joint activities among multiple residents in smart environments , 2010, J. Ambient Intell. Humaniz. Comput..

[20]  Rokia Missaoui,et al.  Building Concept (Galois) Lattices from Parts: Generalizing the Incremental Methods , 2001, ICCS.

[21]  Abdenour Bouzouane,et al.  Complex behavioral pattern mining in non-intrusive sensor-based smart homes using an intelligent activity inference engine , 2017, Journal of Reliable Intelligent Environments.

[22]  Archan Misra,et al.  CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-inhabitant Smart Homes , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[23]  Charu C. Aggarwal,et al.  Data Clustering , 2013 .

[24]  Abdelhamid Bouchachia,et al.  Multi-resident Activity Recognition Using Incremental Decision Trees , 2014, ICAIS.

[25]  Abdenour Bouzouane,et al.  Recognizing multi-resident activities in non-intrusive sensor-based smart homes by formal concept analysis , 2018, Neurocomputing.

[26]  Bernhard Ganter,et al.  Two Basic Algorithms in Concept Analysis , 2010, ICFCA.

[27]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[28]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[29]  Amedeo Napoli,et al.  Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies , 2008, EKAW.

[30]  Rokia Missaoui,et al.  INCREMENTAL CONCEPT FORMATION ALGORITHMS BASED ON GALOIS (CONCEPT) LATTICES , 1995, Comput. Intell..

[31]  Leandro L. Minku.  Transfer Learning in Non-stationary Environments , 2018, Studies in Big Data.

[32]  Mohammed Feham,et al.  Modeling interaction in multi-resident activities , 2017, Neurocomputing.

[33]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[34]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.

[35]  Abdenour Bouzouane,et al.  Real-Time Activity Prediction and Recognition in Smart Homes by Formal Concept Analysis , 2016, 2016 12th International Conference on Intelligent Environments (IE).

[36]  Gregory Ditzler,et al.  Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.