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Autonomous Machine Learning (ALMA):generating rules from data streams

Angelov, Plamen (2011) Autonomous Machine Learning (ALMA):generating rules from data streams. In: Proceedings of the Special International Conference on Complex Systems, COSY-2011. , Ohrid, FYR of Macedonia, pp. 249-256.

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Abstract

In this paper the well known problem of automatically generating tractable models (for example, but not limited to fuzzy rules) from data by learning will be given a new impetus by introduction of the concept of Autonomous Learning MAchines (ALMA). This concept increases the level of automation and autonomy of the process of model identification and design by reducing the need for human intervention in parts of the problem where this is traditionally done manually and off-line such as model structure identification. The proposed concept is generic and is not limited to fuzzy rule based (FRB) systems or neuro-fuzzy (NF) systems, but is also applicable to hidden Markov models (HMM), decision trees between others. For the specific case of FRB and NF models which are considered in this paper, the membership functions (MF) are automatically generated from (and represent) the true data distribution using kernels and data clouds and recursively estimated relative data density. In this paper we propose an original method for evolving clouds from data based on the well known mean-shift approach and we propose an evolving version of it which we call evolving local means (ELM). The proposed ALMA can be used as a basis of software algorithms and agents and hardware devices in a wide range of problems in industry, defense, security, space exploration, robotics, human behavior analysis, assisted living, etc.

Item Type: Contribution in Book/Report/Proceedings
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 52178
Deposited By: ep_importer_pure
Deposited On: 21 Dec 2011 09:29
Refereed?: No
Published?: Published
Last Modified: 21 Mar 2014 10:32
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/52178

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