A supervised machine learning framework with combined blocking for detecting serial crimes

Detecting serial crimes is to find criminals who have committed multiple crimes. A classification technique is often used to process serial crime detection, but the pairwise comparison of crimes is of quadratic complexity, and the number of nonserial case pairs far exceeds the number of serial case pairs. The blocking method can play a role in reducing pairwise calculation and eliminating nonserial case pairs. But the limitation of previous studies is that most of them use a single criterion to select blocks, which is difficult to guarantee an excellent blocking result. Some studies integrate multiple criteria into one comprehensive index. However, the performance is easily affected by the weighting method. In this paper, we propose a combined blocking (CB) approach. Each criminal behaviour is defined as a behaviour key (BHK) and used to form a block. CB learns several weak blocking schemes by different blocking criteria and then combines them to form the final blocking scheme. The final blocking scheme consists of several BHKs. Because rare behaviour can better identify crime series, each BHK is assigned a score according to its rarity. BHKs and their scores are used to determine whether a case pair need to be compared. After comparing with multiple blocking methods, CB can effectively guarantee the number of serial case pairs while greatly reducing unnecessary nonserial case pairs. The CB is embedded in a supervised machine learning framework. Experiments on real-world robbery cases demonstrate that it can effectively reduce pairwise comparison, alleviate the class imbalance problem and improve detection performance.