Scalable approximate FRNN-OWA classification

Fuzzy Rough Nearest Neighbour classification with Ordered Weighted Averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbour search. In practice, the query time complexity of exact nearest neighbour search algorithms in more than a handful of dimensions is near-linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbours returned by Hierarchical Navigable Small Worlds (HNSW), a recent approximative nearest neighbour search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW, and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster

A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

An enhanced resampling technique for imbalanced data sets

A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampling, oversampling, and a combination of both techniques have been widely used. However, the undersampling and oversampling techniques suffer from elimination and addition of relevant data which may lead to poor classification results. Hence, this study aims to increase classification metrics by enhancing the undersampling technique and combining it with an existing oversampling technique. To achieve this objective, a Fuzzy Distancebased Undersampling (FDUS) is proposed. Entropy estimation is used to produce fuzzy thresholds to categorise the instances in majority and minority class into membership functions. FDUS is then combined with the Synthetic Minority Oversampling TEchnique (SMOTE) known as FDUS+SMOTE, which is executed in sequence until a balanced data set is achieved. FDUS and FDUS+SMOTE are compared with four techniques based on classification accuracy, F-measure and Gmean. From the results, FDUS achieved better classification accuracy, F-measure and G-mean, compared to the other techniques with an average of 80.57%, 0.85 and 0.78, respectively. This showed that fuzzy logic when incorporated with Distance-based Undersampling technique was able to reduce the elimination of relevant data. Further, the findings showed that FDUS+SMOTE performed better than combination of SMOTE and Tomek Links, and SMOTE and Edited Nearest Neighbour on benchmark data sets. FDUS+SMOTE has minimised the removal of relevant data from the majority class and avoid overfitting. On average, FDUS and FDUS+SMOTE were able to balance categorical, integer and real data sets and enhanced the performance of binary classification. Furthermore, the techniques performed well on small record size data sets that have of instances in the range of approximately 100 to 800

Fuzzy and smote resampling technique for imbalanced data sets

There are many factors that could affect the performance of a classifier.One of these factors is having imbalanced datasets which could lead to problem in classification accuracy.In binary classification, classifier often ignores instances in minority class.Resampling technique, specifically, undersampling and oversampling are the techniques that are commonly used to overcome the problem related to imbalanced data sets. In this study, an integration of undersampling and oversampling techniques is proposed to improve classification accuracy.The proposed technique is an integration between Fuzzy Distance-based Undersampling and SMOTE.The findings from the study indicate that the proposed combination technique is able to produce more balanced datasets to improve the classification accuracy

A scalable approach to fuzzy rough nearest neighbour classification with ordered weighted averaging operators

Fuzzy rough sets have been successfully applied in classification tasks, in particular in combination with OWA operators. There has been a lot of research into adapting algorithms for use with Big Data through parallelisation, but no concrete strategy exists to design a Big Data fuzzy rough sets based classifier. Existing Big Data approaches use fuzzy rough sets for feature and prototype selection, and have often not involved very large datasets. We fill this gap by presenting the first Big Data extension of an algorithm that uses fuzzy rough sets directly to classify test instances, a distributed implementation of FRNN-OWA in Apache Spark. Through a series of systematic tests involving generated datasets, we demonstrate that it can achieve a speedup effectively equal to the number of computing cores used, meaning that it can scale to arbitrarily large datasets