When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning

Abstract

One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.

An Automatic Adaptive Case-based Reasoning System for Depression Remedy Recommendation

“…On the other hand, other studies used CBR with different techniques. Kwon et al [31] used crowd knowledge with CBR to diagnose depression and stress. Furthermore, Rahim et al [32] used the help of specialists and built an expert system with CBR to diagnose physiological disorders.…”

Creation of a knowledge management model based on CBR

“…Thus, even if it has many qualities, the CBR is not a knowledge-based system that "learns to solve problems by solving problems" because it cannot dynamically evolve its representation of experiences. Three main causes for this limitation are proposed in (Kwon et al, 2018): case model "too well structured and therefore too constrained", the paradox of "fixed knowledge that evolves" and the reductive hypothesis of "it is good for me now so it will always be good for everyone". As for the effectiveness of case-based reasoning (CBR), it is no longer in question when it is necessary to reuse knowledge.…”