Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911778885074944 |
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| author | Lyu, Zimeng Thapa, Pujan Desell, Travis |
| author_facet | Lyu, Zimeng Thapa, Pujan Desell, Travis |
| contents | Identifying phases of flight is important in the field of general aviation, as knowing which phase of flight data is collected from aircraft flight data recorders can aid in the more effective detection of safety or hazardous events. General aviation flight data for phase of flight identification is usually per-second data, comes on a large scale, and is class imbalanced. It is expensive to manually label the data and training classification models usually faces class imbalance problems. This work investigates the use of a novel method for minimally supervised self-organizing maps (MS-SOMs) which utilize nearest neighbor majority votes in the SOM U-matrix for class estimation. Results show that the proposed method can reach or exceed a naive SOM approach which utilized a full data file of labeled data, with only 30 labeled datapoints per class. Additionally, the minimally supervised SOM is significantly more robust to the class imbalance of the phase of flight data. These results highlight how little data is required for effective phase of flight identification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11185 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification Lyu, Zimeng Thapa, Pujan Desell, Travis Machine Learning Neural and Evolutionary Computing Identifying phases of flight is important in the field of general aviation, as knowing which phase of flight data is collected from aircraft flight data recorders can aid in the more effective detection of safety or hazardous events. General aviation flight data for phase of flight identification is usually per-second data, comes on a large scale, and is class imbalanced. It is expensive to manually label the data and training classification models usually faces class imbalance problems. This work investigates the use of a novel method for minimally supervised self-organizing maps (MS-SOMs) which utilize nearest neighbor majority votes in the SOM U-matrix for class estimation. Results show that the proposed method can reach or exceed a naive SOM approach which utilized a full data file of labeled data, with only 30 labeled datapoints per class. Additionally, the minimally supervised SOM is significantly more robust to the class imbalance of the phase of flight data. These results highlight how little data is required for effective phase of flight identification. |
| title | Minimally Supervised Topological Projections of Self-Organizing Maps for Phase of Flight Identification |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2402.11185 |