Plankton is a biotic component at the base of an ecological pyramid and plays an undeniably crucial role in ocean ecosystems and their interconnected environmental dynamics ranging from sustaining marine food webs to influencing the global carbon cycle. Plankton, the collective term encompassing aquatic organisms transported by tides and currents, holds vital insights into these ecosystems. Understanding intricate relationships, distribution patterns, demographic cycles, and their implications for marine food webs and global climate change necessitates detecting, classifying, and monitoring plankton taxa in their ecosystem using specialized imaging devices to collect microscopic samples year-round. Leveraging modern technologies such as machine learning and computer vision, researchers have begun analyzing plankton diversity and abundance. However, the real-world plankton data gathered during monitoring follows an exponential distribution pattern. This non-uniform, class-imbalanced distribution pattern in datasets (including WHOI and NDSB) poses a formidable challenge for classification tasks, especially in identifying rare classes. While a few good attempts have been made to automate the classification of these plankton categories, significant hurdles remain. To address this challenge, we present a novel and systematic approach designed to handle exponentially distributed datasets (specifically WHOI and NDSB) with non-uniform class samples for plankton classification. Departing from traditional methods like resampling and synthetic data generation, we introduce a two-stage complexity-mitigating treatment: Dataset Class Imbalance Treatment (DIT) and Dataset Class-Overlap Treatment (DOT). In the DIT stage, we judiciously prune imbalanced classes based on exclusion criteria we formulated with Ir, and in the DOT stage, we employ our proposed M2 measure to prune classes with overlaps. We then develop the model using this refined dataset for classification. For this purpose, we incorporate a tailored knowledge transfer strategy that involves training and fine-tuning the ResNet Model hyperparameters with an optimizer equipped with a customized cyclic learning rate (CLR) schedule or policy. This strategy enhances our classifier’s ability to grasp new and learned knowledge, producing appreciable outcomes. The results we are achieving are remarkable. [...]
Inhaltsverzeichnis (Table of Contents)
- 1 Introduction
- 1.1 Related works
- 2 Datasets
- 2.1 WHOI
- 2.2 Kaggle NDSB
- 2.3 Dataset characteristics Measure
- 2.3.1 Class-Imbalance Ratio Ir
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The main objective of this work is to develop a novel approach for classifying plankton images from imbalanced datasets, specifically the WHOI and NDSB datasets. The approach aims to improve classification accuracy, particularly for rare plankton classes, without relying on traditional resampling or synthetic data generation techniques.
- Effective classification of imbalanced plankton datasets.
- Development of a two-stage complexity-mitigating treatment (DIT and DOT).
- Application of a tailored knowledge transfer strategy using a ResNet model with a customized cyclic learning rate (CLR).
- Evaluation of the proposed approach using the WHOI and NDSB datasets.
- Quantification of class imbalance and overlap to assess the impact on classifier performance.
Zusammenfassung der Kapitel (Chapter Summaries)
1 Introduction: This chapter introduces the critical role of plankton in the ecosystem and the challenges in accurately classifying plankton using machine learning due to the class imbalance problem inherent in real-world plankton datasets. It highlights the importance of automated plankton classification systems for ecosystem modeling and introduces the research objectives focusing on addressing the class imbalance issue in plankton datasets.
1.1 Related works: This section reviews existing literature on automated plankton classification, focusing on the challenges of class imbalance. It discusses traditional approaches like resampling and data augmentation, as well as recent deep learning-based methods using techniques such as transfer learning and synthetic data generation. The review highlights the limitations of previous approaches and positions the current research as offering a novel, two-stage solution to the class imbalance problem.
2 Datasets: This chapter introduces the WHOI and NDSB plankton datasets, providing details on their characteristics, including class distribution patterns. It emphasizes the challenges posed by the imbalanced and overlapping classes in these datasets, setting the stage for the proposed methodology to address these complexities.
2.1 WHOI: This section details the WHOI Plankton dataset, its acquisition methods (IFCB technology), and its characteristics, including the highly skewed distribution of classes, with one class ("mix") dominating the dataset. The exponential distribution of classes and the challenges this poses for classification are highlighted.
2.2 Kaggle NDSB: This section describes the Kaggle NDSB dataset, its origin, and its characteristics. Although less severely imbalanced than the WHOI dataset, it still presents challenges due to class imbalance and the presence of "junk" classes. The section provides a comparative analysis of NDSB with the WHOI dataset in terms of their class distribution and overall quality.
2.3 Dataset characteristics Measure: This section introduces the measures used to quantify class imbalance (Ir) and class overlap (M2) in the datasets. It describes the rationale behind choosing these measures and explains how they are calculated, highlighting the novelty of the M2 measure proposed in this research. The section provides context for using these metrics to guide the dataset treatment process.
2.3.1 Class-Imbalance Ratio Ir: This subsection provides a detailed explanation of the class imbalance ratio (Ir) metric used in the study to quantify the degree of class imbalance in the plankton datasets. It clarifies the concept of class imbalance from both dataset and algorithmic perspectives and emphasizes the impact of this imbalance on the performance of classifiers.
Schlüsselwörter (Keywords)
Plankton classification, class imbalance, dataset treatment, cyclic learning rate (CLR), ResNet model, WHOI dataset, NDSB dataset, knowledge transfer, overlap measure, imbalance gap, Ir, M2.
Häufig gestellte Fragen
Worum geht es in diesem Dokument?
Dieses Dokument ist eine umfassende Sprachvorschau, die einen Titel, ein Inhaltsverzeichnis, Zielsetzungen und Themenschwerpunkte, Kapitelzusammenfassungen und Schlüsselwörter enthält. Es beschreibt eine Forschungsarbeit zur Klassifizierung von Planktonbildern aus unausgewogenen Datensätzen.
Welche Datensätze werden in dieser Arbeit verwendet?
Die Arbeit verwendet die Datensätze WHOI (Woods Hole Oceanographic Institution) und Kaggle NDSB (National Data Science Bowl) für die Planktonbildklassifizierung.
Was ist das Hauptziel dieser Forschungsarbeit?
Das Hauptziel ist die Entwicklung eines neuen Ansatzes zur Klassifizierung von Planktonbildern aus unausgewogenen Datensätzen. Der Ansatz zielt darauf ab, die Klassifikationsgenauigkeit zu verbessern, insbesondere für seltene Planktongruppen, ohne auf traditionelle Resampling- oder synthetische Datengenerierungstechniken zurückzugreifen.
Was sind die Themenschwerpunkte dieser Arbeit?
Die Themenschwerpunkte umfassen die effektive Klassifizierung unausgewogener Plankton-Datensätze, die Entwicklung einer zweistufigen Komplexitätsreduzierung (DIT und DOT), die Anwendung einer maßgeschneiderten Wissenstransferstrategie mit einem ResNet-Modell mit einer angepassten zyklischen Lernrate (CLR) und die Bewertung des vorgeschlagenen Ansatzes mithilfe der WHOI- und NDSB-Datensätze.
Was ist die Bedeutung von Plankton in diesem Kontext?
Plankton spielt eine entscheidende Rolle im Ökosystem, und eine genaue Klassifizierung ist wichtig für die Modellierung von Ökosystemen. Die automatische Klassifizierung von Planktonbildern mithilfe von maschinellem Lernen wird durch das Problem der Klassenungleichheit in realen Datensätzen erschwert.
Was ist das Problem der Klassenungleichheit (class imbalance)?
Klassenungleichheit tritt auf, wenn die Anzahl der Beispiele für verschiedene Klassen in einem Datensatz stark variiert. Dies kann dazu führen, dass Klassifikatoren Klassen mit weniger Beispielen schlechter erkennen.
Welche Methoden werden zur Quantifizierung der Klassenungleichheit verwendet?
Die Klassenungleichheit wird mithilfe des Class-Imbalance Ratio (Ir) quantifiziert. Das Maß M2 wird für die Messung der Überlappung der Klassen (class overlap) verwendet.
Was ist der Unterschied zwischen den WHOI- und NDSB-Datensätzen?
Der WHOI-Datensatz weist eine stärkere Klassenungleichheit auf als der NDSB-Datensatz. Der WHOI-Datensatz wird durch die Klasse "mix" dominiert, während der NDSB-Datensatz zwar weniger unausgewogen ist, aber immer noch Herausforderungen durch Klassenungleichheit und "Junk"-Klassen bietet.
Was sind die Schlüsselwörter (keywords) dieser Arbeit?
Die Schlüsselwörter umfassen Planktonklassifizierung, Klassenungleichheit, Datensatzbehandlung, zyklische Lernrate (CLR), ResNet-Modell, WHOI-Datensatz, NDSB-Datensatz, Wissenstransfer, Überlappungsmaß, Ungleichgewichtsspalte, Ir, M2.
- Quote paper
- Showkat Ahmad (Author), 2024, Navigating the Depths. Effective Classification of Imbalanced Plankton Classes, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/1523793
