Microarray technology has revolutionized genomics by allowing simultaneous analysis of thousands of gene expressions. However, high-dimensionality and noise in raw intensity microarray data pose challenges in microarray image analysis, spot segmentation and classification. Interval-based vector representation methods address these issues by improving data consistency, reducing unpredictability, and supporting downstream analysis. These methods are crucial in clinical and biological research, aiding in biomarker discovery. Interval-based representation methods for microarray spot vectors result in a robust, noise-resistant approach for high-dimensional genomic data analysis. These methods reduce noise sensitivity and enhance interpretability of gene expression patterns. The research proposes an effective technique for identifying microarray spot vectors using interval-based algorithms which address the problems of high-dimensional microarray datasets. Interval-based representations for gene expression values face several challenges, including a lack of standardized interval construction methods, scalability issues with high-dimensional data, limited comparative studies with other robust classifiers, challenges in handling overlapping intervals, integration with biological knowledge, insufficient exploration of advanced machine learning techniques, need for improved evaluation metrics, and limited real-world applications and validation. There is a gap in constructing intervals for gene expression values with efficient dimensionality reduction techniques. Comparative studies with other robust classifiers have provided insights into their performance. Integrating domain knowledge could improve classification accuracy. The objective of this research work is to overcome all the above-mentioned gaps with three interval-based methods viz–user-defined custom interval-based representation, quantile-based representation, and clustering-based interval representation. The methodology proposes Principal Component Analysis (PCA) for feature selection as microarray spot vector dataset is of high dimensional. The interval-based represented spot vector is used in further stage of classification technique that is Soft-Margin Support Vector Machine (SVM). The experiments have resulted in promising improvement of classification efficiency that helps in gene profiling and diagnosis accurately.