A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines

TL;DR

This paper introduces a novel method for generating immunological memory in the Clonal Selection Algorithm using Restricted Boltzmann Machines to enhance image feature extraction and landmark detection accuracy.

Contribution

It proposes a new approach combining RBMs with CSAIM to improve landmark detection in image data from participatory sensing.

Findings

Improved detection accuracy of landmarks.

Effective feature extraction from natural language and image data.

Enhanced classification performance in experiments.

Abstract

Recently, a high technique of image processing is required to extract the image features in real time. In our research, the tourist subject data are collected from the Mobile Phone based Participatory Sensing (MPPS) system. Each record consists of image files with GPS, geographic location name, user's numerical evaluation, and comments written in natural language at sightseeing spots where a user really visits. In our previous research, the famous landmarks in sightseeing spot can be detected by Clonal Selection Algorithm with Immunological Memory Cell (CSAIM). However, some landmarks was not detected correctly by the previous method because they didn't have enough amount of information for the feature extraction. In order to improve the weakness, we propose the generation method of immunological memory by Restricted Boltzmann Machines. To verify the effectiveness of the method, some experiments for classification of the subjective data are executed by using machine learning tools for Deep Learning.