No. | Forecasting method | Accuracy | Limitations | Observation | References |
---|---|---|---|---|---|
1 | Decision tree (J48) | 59.15% | Took more time of 0.76 s to build model compared to 0.09 s of other models. | They took J48 naïve Bayesian and ZeroR and compared them by running tests. | [58] |
2 | KNN (K = 5) | 66.6939% | Data filling algorithms needs to be added to increase the accuracy. | In their research they try to prove that higher accuracy can be achieved if GBWKNN filling algorithm and KNN classification algorithm is combined. | [56] |
3 | KNN (K = 10) | 87.03% | Naïve Bayesian has slightly higher accuracy. | They essentially divided data into critical and non-critical and then compared it in 5 classification algorithms and noted that naïve Bayesian, neural networks, and KNN predict better than the SVM and decision tree. | [57] |
4 | Naïve Bayes classifier | 87.00% | Cannot be applied to the dataset having large number of features. | They implemented a novel crime detection naïve Bayes method for crime prediction and analysis. | [60] |
5 | Decision tree | 83.9519% | As they are unstable, a small change in data can lead to a large change in the structure. | They showed that decision tree performed better than the naïve Bayesian with the same crime dataset, using WEKA. | [59] |
6 | Naïve Bayes | 65.59% | Computational speed, robustness, scalability, and interpretability were not taken into consideration. | This paper presented comparative analysis on the accuracy of k-NN, naive Bayescand decision tree algorithms in predicting crimes and criminal actions. | [61] |
7 | Autoregressive integrated moving average (ARIMA) | The mean absolute error and standard deviation of the model are (1) Test average test standard deviation is 0.0867 ± 0.0293; (2) Training average training standard deviation is 0.0413 ± 0.0084. | They have described this model as quite complex compared to others. | This paper is about the cons of fuzzy cognitive maps with respect to time series prediction. The ARIMA uses the auto-correlation parameters. | [62] |
8 | Regression model | They first took 10 crimes per month the expected forecast for absolute percent error (APE) was 42%. When 20 crimes were taken the expected forecast APE was 28%, and at 25 crimes per month the expected forecast APE was 25%. After 30 crimes they 13.5% error. | This research date backs 20 years. | They aim at predicting crimes 30 days ahead. They conduct the experiment in Pittsburgh. | [63] |
9 | SVM | Over 10 months of experiment its accuracy was 84.37%. | The challenge that they indicated that we may face in the future could be to locate the best point at which spatial knowledge is available. | They compare different model to analyze which has the best chance at predicting hotspots. | [64] |
10 | Random Forrest Regressor | Ninety-seven percent accuracy in predicting crimes | They got this high accuracy in previous recorded crimes to actually predict crimes in real life will be a challenge. | They had divided their data into 2 parts 80% of it was used to train the model and the rest 20% was used to test the model in this the model achieved the score of 90%. | [65] |