Feature selection generating directed rough-spanning tree for crime pattern analysis

Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization strategies. Further, we use a graph based unsupervised machine learning technique called core periphery structures to analyze how crime behavior evolves over time. These methods can be generalized to use for different counties and can be greatly helpful in planning police task forces for law enforcement and crime prevention.

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GIS and crime pattern analysis

GIS and the Social Sciences ◽

10.4324/9781315759326-7 ◽

2017 ◽

pp. 151-165

Author(s):

Dimitris Ballas ◽

Graham Clarke ◽

Rachel S. Franklin ◽

Andy Newing

Keyword(s):

Pattern Analysis ◽

Crime Pattern

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A Supervised Feature Selection Algorithm through Minimum Spanning Tree Clustering

2014 IEEE 26th International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2014.47 ◽

2014 ◽

Cited By ~ 3

Author(s):

Qin Liu ◽

Jingxiao Zhang ◽

Jiakai Xiao ◽

Hongming Zhu ◽

Qinpei Zhao

Keyword(s):

Feature Selection ◽

Spanning Tree ◽

Minimum Spanning Tree ◽

Selection Algorithm ◽

Feature Selection Algorithm

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Crime Pattern Analysis by Identifying Named Entities and Relation Among Entities

Advances in Intelligent Systems and Computing - Advanced Computational and Communication Paradigms ◽

10.1007/978-981-10-8237-5_8 ◽

2018 ◽

pp. 75-84 ◽

Cited By ~ 2

Author(s):

Priyanka Das ◽

Asit Kumar Das

Keyword(s):

Pattern Analysis ◽

Named Entities ◽

Crime Pattern

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AN EVOLUTIONARY APPROACH TO MULTIVARIATE FEATURE SELECTION FOR FMRI PATTERN ANALYSIS

Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing ◽

10.5220/0001064203020307 ◽

2008 ◽

Keyword(s):

Feature Selection ◽

Pattern Analysis ◽

Evolutionary Approach ◽

Selection For

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Dysphonic Voice Pattern Analysis of Patients in Parkinson’s Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/4201984 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Yunfeng Wu ◽

Pinnan Chen ◽

Yuchen Yao ◽

Xiaoquan Ye ◽

Yugui Xiao ◽

...

Keyword(s):

Feature Selection ◽

Pattern Analysis ◽

Characteristic Curve ◽

Support Vector ◽

Linear Discriminant ◽

Leibler Divergence ◽

Ensemble Algorithm ◽

Highly Correlated ◽

Feature Selection Approach ◽

Bagging Ensemble

Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, amplitude variations, and nonlinear measures. The highly correlated vocal parameters are combined by using the linear discriminant analysis method. Based on the probability density functions estimated by the Parzen-window technique, we propose an interclass probability risk (ICPR) method to select the vocal parameters with small ICPR values as dominant features and compare with the modified Kullback-Leibler divergence (MKLD) feature selection approach. The experimental results show that the generalized logistic regression analysis (GLRA), support vector machine (SVM), and Bagging ensemble algorithm input with the ICPR features can provide better classification results than the same classifiers with the MKLD selected features. The SVM is much better at distinguishing normal vocal patterns with a specificity of 0.8542. Among the three classification methods, the Bagging ensemble algorithm with ICPR features can identify 90.77% vocal patterns, with the highest sensitivity of 0.9796 and largest area value of 0.9558 under the receiver operating characteristic curve. The classification results demonstrate the effectiveness of our feature selection and pattern analysis methods for dysphonic voice detection and measurement.

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