IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning

Details IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning

2011-06-30

arxiv.org/abs/1106.6186v1

International Journal of Computer Science & Emerging Technologies (E-ISSN: 2044-6004) 74 Volume 1, Issue 4, December 2010

Computer Science

Learning

Keywords: Self-evolving algorithm; machine learning; decision-trees; learning algorithms, Hidden Markov Models; http://ijcse

Scientific paper

We present IBSEAD or distributed autonomous entity systems based Interaction - a learning algorithm for the computer to self-evolve in a self-obsessed manner. This learning algorithm will present the computer to look at the internal and external environment in series of independent entities, which will interact with each other, with and/or without knowledge of the computer's brain. When a learning algorithm interacts, it does so by detecting and understanding the entities in the human algorithm. However, the problem with this approach is that the algorithm does not consider the interaction of the third party or unknown entities, which may be interacting with each other. These unknown entities in their interaction with the non-computer entities make an effect in the environment that influences the information and the behaviour of the computer brain. Such details and the ability to process the dynamic and unsettling nature of these interactions are absent in the current learning algorithm such as the decision tree learning algorithm. IBSEAD is able to evaluate and consider such algorithms and thus give us a better accuracy in simulation of the highly evolved nature of the human brain. Processes such as dreams, imagination and novelty, that exist in humans are not fully simulated by the existing learning algorithms. Also, Hidden Markov models (HMM) are useful in finding "hidden" entities, which may be known or unknown. However, this model fails to consider the case of unknown entities which maybe unclear or unknown. IBSEAD is better because it considers three types of entities- known, unknown and invisible. We present our case with a comparison of existing algorithms in known environments and cases and present the results of the experiments using dry run of the simulated runs of the existing machine learning algorithms versus IBSEAD.

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