The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries

Abstract

Machine learning is well developed amongst the scientific community in terms of theoretical foundations (statistics and algorithms) and frameworks (Tensorflow, PyTorch, H2O). However, machine learning is heavily focused on numerical data, or numerical data mixed with some categorical data. For numerical datasets, scientists and engineers can enjoy reasonable success with only a limited knowledge of theoretical foundations and the inner workings of machine learning frameworks. However, it is a different story when dealing with purely categorical datasets, which require a deeper understanding of machine learning frameworks and associated encodings and algorithms in order to achieve success. This paper addresses the issues in handling purely categorical datasets for multi-classification problems and provides a set of heuristics for dealing with purely categorical data. In particular, issues such as pre-processing, feature encoding and algorithm selection are considered. The heuristics are then demonstrated through a case study, based on a categorical data set of domestic fire injuries, covering a 10-year period. Novel contributions are made through the heuristics and the performance analysis of different encoding techniques. The case study itself also makes a novel contribution through the classification of different types of injuries, based on related features.

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

“…The second methodology involves literature reviews and analysis of the application of AI algorithms to different context to obtain a set of structured guidelines and heuristics [28,29,31,32]. Finally, authors in [33] created an ontology to support the algorithm recommendation process.…”

“…3. From the nine selected papers, two of them belong to the early 2000s period [25,26], while the remaining belong to the last six years interval [27][28][29][30][31][32][33].…”

“…AI algorithm selection approaches could employ different aspects that influence the recommendation process. In this case, from the nine selected papers, we identified two main factors: dataset characteristics (present in every selected work) but also the task to be carried out through AI, present in [29,32], or the algorithm requirements and characteristics [28,31]. Table 2 summarizes the results.…”

“…As can be seen in Fig. 5, the target AI algorithms of the recommendation process are mostly framed in the "classification" category [25,26,28,[30][31][32]. But there are some papers that also provide heuristics for clustering [28], regression [28,32], and feature selection problems [27,33].…”

Particle Swarm Optimization Based Fire Risk Valuation Model: Shopping-Mall

“…It is difficult to exactly and timely forecast the fire risk level [9] [10]. In recent years, with the progress of artificial intelligence (AI) technology, the real-time data acquisition Open Journal of Safety Science and Technology system based on sensor networks can use intelligent methods to build a multi-factor building fire risk rating forecast model and carry out accurate and real-time small-scale prediction, which is of great significance for effectively protecting shopping malls resources in key areas of building fire prevention and control and saving malls fire prevention and control funds [11] [12] [13]. In this article, we comprehensively consider the following factors:…”