Etiketlenmiş Verilerin Kıtlığı Durumunda Suç Oluşumunun Tahmini

Year 2021, , 677 - 687, 24.05.2021

Volkan Kıranoglu , Göksu Tüysüzoğlu , Elife Öztürk Kıyak

https://doi.org/10.21205/deufmd.2021236828

Cited By: 2

Abstract

Teknolojik gelişmeler doğrultusunda, makine öğrenmesi/veri madenciliği çalışmaları suç analizinde önemli ölçüde artmıştır. Suç olaylarının tahmini, ceza davalarının mekansal/zamansal dağılımının tespiti, suç türünün öngörülmesi bu çalışma alanlarından bazılarıdır. Suç oranlarındaki önemli artıştan kaynaklanan suç verileri dikkate alındığında, gelecekteki olaylar için suç kalıplarını araştırmak veya suçla ilgili tahminleri kolayca yapmak için etiketlenmemiş veriler kullanılabilir. Bu nedenle, bu çalışmada, 2019 polis verilerini kullanarak İngiltere'de suç araştırmalarının sonucunu tahmin etmek için aktif öğrenme, kendi kendine öğrenme ve rastgele örnekleme teknikleri uygulanmıştır. Deneysel analize göre, aktif öğrenme, çok az etiketlenmiş veri olması durumunda düzensizliğe dayalı akıllı seçim stratejisini kullanarak muadillerinden daha iyi performans göstermektedir.

Keywords

Aktif Öğrenme, Sınıflandırma, Suç Tespiti, Rastgele Örnekleme, Yarı Denetimli Öğrenme, Kendi Kendine Öğrenme

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Abstract

In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, self-learning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

Keywords

Active Learning, Classification, Crime Detection, Random Sampling, Semi-Supervised Learning, Self-Learning

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