Incremental Search for Informative Gene Selection in Cancer Classification

Fajila, Fathima; Yusof, Yuhanis

doi:10.33166/aetic.2021.02.002

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…13. end while 14. Return the best set of features F= argmaxbf b as the final output Similarly, the existence of Firefly algorithm is also evident from the related work section and also the studies that have been referred to in citations [25,26].…”

Section: D) Limited Exploration Of Hybrid Models In Financial Predict...mentioning

confidence: 98%

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

Gulande,

Awale

2024

CSSE

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The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm algorithm, fusing the capabilities of the Artificial Bee Colony (ABC) and Firefly algorithms, aimed at improving feature selection in gene expression classification. The proposed method undergoes rigorous validation through statistical machine learning techniques and quantitative parameter evaluation, with comprehensive comparisons to established techniques in the field. The findings underscore the superiority of the hybrid Swarm Intelligence approach for feature selection in gene expression classification, offering promising prospects for enhancing cancer diagnosis and prognosis.

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Section: D) Limited Exploration Of Hybrid Models In Financial Predict...mentioning

confidence: 98%

Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm

Gulande,

Awale

2024

CSSE

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“…[7], and healthcare e.g. [8]. Therefore, proposal of any novel way through which machines could learn to classify has an enormous potential of application in wide variety of areas.…”

Section: Introductionmentioning

confidence: 99%

The Theory of Probabilistic Hierarchical Learning for Classification

Ursani¹,

Ursani²

2023

AETiC

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Providing the ability of classification to computers has remained at the core of the faculty of artificial intelligence. Its application has now made inroads towards nearly every walk of life, spreading over healthcare, education, defence, economics, linguistics, sociology, literature, transportation, agriculture, and industry etc. To our understanding most of the problems faced by us can be formulated as classification problems. Therefore, any novel contribution in this area has a great potential of applications in the real world. This paper proposes a novel way of learning from classification datasets i.e., hierarchical learning through set partitioning. The theory of probabilistic hierarchical learning for classification has been evolved through several works while widening its scope with each instance. The theory demonstrates that the classification of any dataset can be learnt by generating a hierarchy of learnt models each capable of classifying a disjoint subset of the training set. The basic assertion behind the theory is that an accurate classification of complex datasets can be achieved through hierarchical application of low complexity models. In this paper, the theory is redefined and revised based on four mathematical principles namely, principle of successive bifurcation, principle of two-tier discrimination, principle of class membership and the principle of selective data normalization. The algorithmic implementation of each principle is also discussed. The scope of the approach is now further widened to include ten popular real-world datasets in its test base. This approach does not only produce their accurate models but also produced above 95% accuracy on average with regard to the generalising ability, which is competitive with the contemporary literature.

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