A hybrid ABC-SVM approach for multi-dimensional data classification with synthetic data balancing

Objective: Data Mining is one of the majority inspiring areas of research to be develop into more and more accepted in health care organization. Advance structures of classifiers from imbalanced datasets are described. Class imbalance is a vital difficulty in machine learning and occurs in many domains most medical datasets are not balanced in their class labels. Usual classifiers do not carry out well when allowing for data at risk to both within-class and between-class imbalances. Methodology: Most obtainable classification methods tend not to do well on minority class examples when the dataset is very imbalanced. His research paper proposes the result of the accurateness of the result by using the Priority Based Class Clustered under sampling Technique approaches for imbalanced data classification. Findings: In attendance variations of the Adaptive K-means cluster analysis such that the imbalanced nature of the problem is openly addressed in the new algorithm formulation. Improvements: The present paper proposes a cluster-based priority under-sampling approach to select the representative data as training data to get better categorization and correctness for minority class to examine the result of under-sampling methods in the imbalanced class distribution environment. Keywords: Class, wClustering, Data Mining, Imbalanced Data, PBCCUT

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

Despite the considerable efforts that are done for Microarray gene expression profile, biomarker discovery or gene selection process remains a complex challenge for biologists and computer scientists. This article presented an Artificial Bee Colony (ABC) based approach for accurate classification of cancer microarray data using support vector machine (SVM) as a classifier. The approach is able to deal with the high dimensionality of the microarray data. According to literature for microarray data analysis, up to our knowledge, this is the first attempt to apply Artificial Bee Colony based algorithm as gene selection method for cancer classification problem using Microarray gene expression profile. Comparing with other algorithms that are proposed in the literature, we can conclude that our proposed algorithm (ABC-SVM) obtains promising results for gene selection and cancer classification problems.

Membership Weight Salp Swarm Algorithm (MWSSA) based feature selection and deep learning approach for breast cancer classification of SNP genomics data

An Adaptive Harmony Search Approach for Gene Selection and Classification of High Dimensional Medical Data

In this paper, we study how to use semantic relationships for image classification in order to improve the classification accuracy. We achieve the goal by imitating the human visual system which classifies categories from coarse to fine grains based on different visual features. We propose an ontological bagging algorithm where most discriminative weak attributes are automatically learned for different semantic levels by multiple instance learning and the bagging idea is applied to reduce the error propagations of hierarchical classifiers. We also leverage ontological knowledge to augment crowdsourcing annotations (e.g., a hatchback is also a vehicle) in order to train hierarchical classifiers. Our method is tested on a vehicle dataset from the popular crowdsourcing dataset ImageNet. Experimental results show that our method not only achieves state-of-the-art results but also identifies semantically meaningful visual features.

Solar power generation is one of the renewable energy sources that has gained prominence in recent times. Power generation companies and researchers are interested in the behavior of solar facilities with respect to daily weather fluctuations over time. Researchers have often tried to cluster solar spatio-temporal data. Some of the proposed methods contain unnecessary ambiguity with no clear justifications of the applicability of their models. This study aims to provide an accurate cluster-based representation of solar Photovoltaic (PV) production profiles, using the solar data from the University of Louisiana at Lafayette's 1.1 Megawatt(MW) Photovoltaic Applied Research and Testing Lab (PART lab) over a two-year interval. The availability of these clusters provides an easier basis for testing and modelling the behavior and response of an existing solar plant in the event of an upgrade, or incorporation into a power generating facility and solar forecasting. In this study,a machine learning approach is used to generate a clustering algorithm, employing three different existing accuracy metrics. A efficient Dynamic Time-Warping (DTW) k-Means model was developed to cluster the PV data with reasonable accuracy. The novelty of this study provides an efficient temporal-adaptive multi-model approach of clustering solar PV data. These clusters can reduce the computational expense of solar-connected microgrid modelling and simulation studies at the design and operational stages.

The joint classification of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data holds significant importance for various practical uses, including urban mapping, mineral prospecting, and ecological observation. Achieving robust and transferable feature representations is essential to fully leverage the complementary properties of HSI and LiDAR modalities. However, existing methods are often constrained to scene-specific training and lack generalizability across datasets, limiting their discriminative power. To tackle this challenge, we introduce a new dual-phase approach for the combined classification of HSI and LiDAR data. Initially, a transformer-driven network is trained on various HSI-only datasets to extract universal spatial–spectral features. In the second stage, LiDAR data is incorporated as a task-specific prompt to adapt the model to HSI-LiDAR scenes and enable effective multimodal fusion. Through extensive testing on three benchmark datasets, our framework proves highly effective, outperforming all competing approaches.

Multistage approach for clustering and classification of ECG data

In this paper, we present a semi-supervised rough set-based random forest gene selection method for classification of data patterns. The proposed method tries to find the genes of interest known as significant genes and maximise the accuracy of the model with reduction percentage. The advantage of this approach is analysed by experimental results on three benchmark datasets such as leukaemia, colon cancer and SRBCT and results showed an improved accuracy over existing methods such as support vector machine, k-nearest neighbour and random forest. Finally, the performance of those selected significant genes has been measured using classifier validity and statistical measures. The experimental results and performance measures proves the efficiency of the proposed hybridised technique over traditional random forest method.

This paper proposes a novel approach for unsupervised classification of variable length sequence data using a concept inspired from the Particle Swarm Optimization and rational kernel. The approach uses the distance estimated by the rational kernel as a similarity measure used for clustering the particles. It does not require the normalization of the data sequences into fixed size vectors. Each data sequence has a corresponding particle which moves in the parameter space towards other particles with the similar fitness value. Velocity factor which is used in updating particle position is influenced by the rational distance below a specified threshold. Experimental results display the robustness of proposed algorithm. Misclassification error for clustering the particles into different classes is provided in the results section.

Companies want to extract value from their relational databases. This is the aim of relational data mining. Propositionalization is one possible approach to relational data mining. Propositionalization adds new attributes, called features, to the main table, leading to an attribute-value representation, a single table, on which a propositional learner can be applied. However, current relational databases are large and composed of mixed, numerical and categorical, data. Moreover, the specificity of relational data is to involve one-to-many relationships. As an example of such data, consider customers purchasing products: each customer can purchase several products. Therefore, there is a need for techniques able to learn complex aggregates. Learning such features means to explore a combinatorial, possibly infinite, space and such an approach is prone to overfitting. We introduce a propositionalization approach dedicated to a robust Bayesian classifier. It efficiently samples a given number of features in the language bias, following a distribution over the complex aggregates. This distribution is also used to penalize complex aggregates in the regularization of the robust Bayesian classifier. Experiments show that it performs better than state-of-the-art methods on most investigated benchmarks and can deal with large datasets more easily. A new real, large, mixed relational dataset is introduced which confirms the ability of our approach to learn complex aggregates.

The wind turbine sound emission and its propagation is a complex topic, which requires measurement-based investigations. Between 2018 and 2020, five extensive measurement campaigns were carried out at different locations, which lasted in total over 13 month and included the acquisition of meteorological, acoustic and turbine-specific data. Evaluating these large data sets, effective approaches for data assessment, data selection and data classification are essential. In this contribution, these approaches are explained in detail using the example of one measurement campaign and are applied investigating the refraction effects in the sound propagation of wind turbines based on measured data. Accordingly, the refraction effects are clearly visible at a distance of 500 m from the turbine. Thus, higher levels are measured in the downwind direction than in the upwind direction.