A Classification Model with Cognitive Reasoning Ability
In this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea is to use...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/5/1034 |
| _version_ | 1797495047023755264 |
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| author | Jinghong Wang Daipeng Zhang Lina Liang |
| author_facet | Jinghong Wang Daipeng Zhang Lina Liang |
| author_sort | Jinghong Wang |
| collection | DOAJ |
| description | In this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea is to use cognitive reasoning mechanism proposed in this paper to solve the classification problem of large structured data with multiple features and strong correlation between features, and then implements cognitive reasoning for features. The model has three parts. The first part proposes a Feature-to-Image algorithm for converting structured data into image data. The algorithm quantifies the dependencies between features, so as to take into account the impact of individual independent features and correlations between features on the prediction results. The second part designs and implements low-level feature extraction of the quantified features using convolutional neural networks. With the relative symmetry of the capsule network, the third part proposes a cognitive reasoning mechanism to implement high-level feature extraction, feature cognitive reasoning, and classification tasks of the data. At the same time, this paper provides the derivation process and algorithm description of cognitive reasoning mechanism. Experiments show that our model is efficient and outperforms comparable models on the category prediction experiment of ADMET properties of five compounds.This work will provide a new way for cognitive computing of intelligent data analysis. |
| first_indexed | 2024-03-10T01:42:55Z |
| format | Article |
| id | doaj.art-0bef72ccc29841c0aac83f8140d49799 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T01:42:55Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-0bef72ccc29841c0aac83f8140d497992023-11-23T13:20:21ZengMDPI AGSymmetry2073-89942022-05-01145103410.3390/sym14051034A Classification Model with Cognitive Reasoning AbilityJinghong Wang0Daipeng Zhang1Lina Liang2College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, ChinaCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, ChinaCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, ChinaIn this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea is to use cognitive reasoning mechanism proposed in this paper to solve the classification problem of large structured data with multiple features and strong correlation between features, and then implements cognitive reasoning for features. The model has three parts. The first part proposes a Feature-to-Image algorithm for converting structured data into image data. The algorithm quantifies the dependencies between features, so as to take into account the impact of individual independent features and correlations between features on the prediction results. The second part designs and implements low-level feature extraction of the quantified features using convolutional neural networks. With the relative symmetry of the capsule network, the third part proposes a cognitive reasoning mechanism to implement high-level feature extraction, feature cognitive reasoning, and classification tasks of the data. At the same time, this paper provides the derivation process and algorithm description of cognitive reasoning mechanism. Experiments show that our model is efficient and outperforms comparable models on the category prediction experiment of ADMET properties of five compounds.This work will provide a new way for cognitive computing of intelligent data analysis.https://www.mdpi.com/2073-8994/14/5/1034ADMET propertiesfeature-to-imagelow-level feature extractionhigh-level feature extractioncognitive reasoning mechanismcapsule network |
| spellingShingle | Jinghong Wang Daipeng Zhang Lina Liang A Classification Model with Cognitive Reasoning Ability Symmetry ADMET properties feature-to-image low-level feature extraction high-level feature extraction cognitive reasoning mechanism capsule network |
| title | A Classification Model with Cognitive Reasoning Ability |
| title_full | A Classification Model with Cognitive Reasoning Ability |
| title_fullStr | A Classification Model with Cognitive Reasoning Ability |
| title_full_unstemmed | A Classification Model with Cognitive Reasoning Ability |
| title_short | A Classification Model with Cognitive Reasoning Ability |
| title_sort | classification model with cognitive reasoning ability |
| topic | ADMET properties feature-to-image low-level feature extraction high-level feature extraction cognitive reasoning mechanism capsule network |
| url | https://www.mdpi.com/2073-8994/14/5/1034 |
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