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Automated biological data analysis, processing, and synthesis are an active research domain. Massive amount of the biological data produced and stored continuously in biological studies. Information retrieving and analysis from these large sizes of generated data is a challenging issue as well as a critical factor for successful knowledge discovering. Analytical and mathematical approaches used to mine organized and unorganized biological data from various volumes of data. However, it is challenging to conquer hidden and unknown significant data from multiple databases irrespective to their sizes and positions. Hence, mining algorithms, techniques, processes, tools and frameworks are equally valuable for data acquisition and information mining. Recently, the accumulations of biological information and datasets have been rising dramatically due to the development of such tools. The rapid increase of bioinformatics and microbiological datasets appealed to the dependence of computers for the arrangement, computation and synthesis of the datasets. Among the different computational approaches, parallel processing is more effective in biological data operation (next-generation sequencing, genome analysis, etc.) and new data set generation (Kibegwa, F. M., Bett, R. C., Gachuiri, C. K., Stomeo, F., & Mujibi, F. D. (2020)( 2Li, C. X., Li, W., Zhou, J., Zhang, B., Feng, Y., Ping Xu, C., Lu, Y. Y., Holmes, E. C., & Shi, M. (2020)( 3Sangphukieo, A., Laomettachit, T., & Ruengjitchatchawalya, M. (2020)(Kopf et al., 2015).
Diversity of human, animal and plant life/behavior is one of the primary reasons behind the availability of the biological datasets. Despite the variations, the biological functionalities interrelated with sets of living organs that are common in-universe. These organs solely depend on the different microbiological functionalities of proteins, DNA (Deoxyribonucleic acid), and RNA (ribonucleic acid). Microbiological/metagenomic data analysis processes simpler illustrations and sequencing for a metagenomic dataset for vast microbial data diversity (Villar et al., 2015), (Sunagawa et al., 2015). There are several types of metagenomic datasets that are dominant in biological data mining. For large dataset mining, there are large-scale genome sequences that require efficient representations and processing. Consequently, high-performance algorithms and techniques are demanding in biological data analysis and research (Lu et al., 2015)(Li et al., 2015)(De Cruz et al., 2015).
Classification is a significant approach for massive biological data analysis and mining. The classification system includes a sequential combination of multiple methods used to build an efficient solution to deal with particular and correlated data classification. Meta-learning classifiers are recently popular in genome researchers (Statnikov et al., 2005). Such learning classifiers refer to manipulate a set of base predictors for a given classification task and then correlate the output information by using an integration technique. Association rules or classification rules generated under different names such as classifier ensembles, consensus aggregation, decision combination, classifier fusion, hybrid methods and more (Kuncheva, 2002), (Dasarathy, 1994). To improve the performance of a single classifier, the priority assigned to the association or classification rules. Different types of classifiers usually make different predictions outcome on the same sample set of data. Data diversity illustrated that the sets of misclassified samples are classified from different uncorrelated sources by using multiple sets of classifiers. To classify various data diversity used for priority association table. The techniques to develop priority association table divided into two categories, namely, classifiers disturbance and sample disturbance. The first approach of the classifier utilizes the instability of the base classifiers. These classifiers are very sensitive to the initialization parameters. Such classifiers include machine learning, neural networks, random forests, and decision trees. The second approach of the classifiers trains the feature of data sets with different sample subsets.