One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

A survey on pre-processing techniques: relevant issues in the context of environmental data mining

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.Peer ReviewedPostprint (author's final draft

Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends