The Use of Clustering and Classification Methods in Machine Learning and Comparison of Some Algorithms of the Methods

In this article, two machine learning methods such as classification and clustering are used for decision tree (DT), artificial neural network (ANN), and K-nearest neighbors algorithms. The datasets were used to evaluate the effectiveness of the clustering method and the data mining tool. Weather data were used to compare algorithms and methods in the study. This study showed that the best model was DT according to accuracy and precision measures but the best model according to F-measure and receiver operating characteristic curve area measures was ANN. Waikato Environment for Knowledge Analysis, a data mining tool, is utilized in this paper to carry out the clustering

Hyperparameter optimisation for improving classification under class imbalance

Although the class-imbalance classification problem has caught a huge amount of attention, hyperparameter optimisation has not been studied in detail in this field. Both classification algorithms and resampling techniques involve some hyperparameters that can be tuned. This paper sets up several experiments and draws the conclusion that, compared to using default hyperparameters, applying hyperparameter optimisation for both classification algorithms and resampling approaches can produce the best results for classifying the imbalanced datasets. Moreover, this paper shows that data complexity, especially the overlap between classes, has a big impact on the potential improvement that can be achieved through hyperparameter optimisation. Results of our experiments also indicate that using resampling techniques cannot improve the performance for some complex datasets, which further emphasizes the importance of analyzing data complexity before dealing with imbalanced datasets.Algorithms and the Foundations of Software technolog

Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics have been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.Comment: 23 pages, 9 figures, preprin