A Model Selection Method for Machine Learning by Differential Evolution

Chiu, Yi-Chuan; Lin, Hsiu‐Hau; Jou, Yung‐Tsan

doi:10.1145/3335484.3335505

Cited by 1 publication

(1 citation statement)

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“…In the field of machine learning, most of the developers are exercising different models and fining the suitable one at the end for the given problem. Chiu, focused on different regression models and compared the performance of the differential evolution algorithm to that of the genetic algorithm [2]. Using three benchmark datasets, the results show that the differential evolution method beats the genetic algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

OCL Based Approach for Sustainable ML Model Development

Hanumanthu,

Lendale,

Ramesh

et al. 2023

E3S Web Conf.

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It became a bottleneck for the Machine Learning (ML) researchers to select/develop a sustainable model for a particular problem. Hence, there is a need for an approach to prepare a model with all constraints of the software system. The proposed approach is based on Object Constraint Language (OCL) which is a declarative language for writing constraints on software artifacts, it is widely used for effective representation of Functional Requirements (FR’s) and Non-Functional Requirements (NFR’s). In the proposed system, the paddy leaf disease identification system is considered and proposed Leaf Identification Constraints (LIC) and Leaf Disease Identification Constraints (LDIC) based on OCL, for the proposed constraints the Convolutional Neural Network (CNN) is chosen, as it can handle diverse range of input data and large volume of concurrent requests. To satisfy other constraints of the system, the Auto encoders are used along with CNN and the input data was take in the form of thermal imaging. This system was evaluated with test data and validation data and obtained the accuracy of 90.6%. And 84.8 was attained by earlier researchers before this approach.

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Section: Literature Surveymentioning

confidence: 99%