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

Das, Priyanka; Das, Asit Kumar; Nayak, Janmenjoy

doi:10.1007/s00521-018-3880-8

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…For the evaluation purpose, the authors have assessed the generated clusters representing different crime aspects with respect to the ground truth clusters obtained by domain experts. External cluster evaluation techniques [25] like Purity (Pr), Precision (P), Recall (R), and F-measure (F) and Random Index (RI) have been computed using (3 -7):…”

Section: B Evaluation Resultsmentioning

confidence: 99%

A Graph Based Clustering Approach for Relation Extraction From Crime Data

Das

Nayak³

et al. 2019

IEEE Access

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Application of natural language processing techniques based on crime data can prove to be beneficial in several processes of the criminal justice industry. The availability of massive crime reports helps law enforcement agencies when a criminal investigation is launched. While investigating a crime, questions like what type of crime, who committed the crime, what happened at which place, on what time, and what actions are taken, keep arising. Now, it is not feasible for the law enforcement agencies to get into the detail of these available massive crime reports and get the answers. To tackle these problems associated with criminal justice industry, the proposed work considers a textual corpus containing information of crime against women in India and extracts substantial relations between the named entities present in the corpus by a hierarchical graph-based clustering technique. For extracting the relations, different types of entity pairs have been chosen and similarities among them have been measured based on the intermediate context words. Depending on the similarity score, a weighted graph has been formed and a similarity threshold is set to partition the graph based on the edge weights. With the iterative application of the clustering algorithm, all the named entity pairs are grouped into clusters, each of which signifies different crime aspects. Each cluster is characterized using the most frequent context word present in it. The proposed relation extraction scheme helps in crime pattern analysis that can aid in various criminal investigation requirements. The results with optimal cluster validation indices depict the effectiveness of this method.

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Section: B Evaluation Resultsmentioning

confidence: 99%

A Graph Based Clustering Approach for Relation Extraction From Crime Data

Das

Nayak³

et al. 2019

IEEE Access

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“…The comparison is done based on number of features selected and the accuracy of the classifiers used. The feature selection algorithms used are (i) Rough-spanning tree based feature selection algorithm (RMST) [43], (ii) Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS) [55] (iii) Relevant feature selection and ensemble classifier design using bi-objective genetic algorithm (RFSA) [56], (iv) Acoustic feature selection for automatic emotion recognition from speech (AFSS) [57], (v) Exploring boundary region of rough set theory for feature selection (RSFS) [52], and (vi) Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm (SFGA) [58]. To measure the accuracy of the classifiers based on reduced feature set, we have considered eight different classifiers, namely Support vector machine (SVM), K -nearest neighbors (KNN), Decision tree (DT), Neural network (NN), Random forest (RF), Naïve Bayes (NB), Adaboost (BST), and Sequential minimal optimization (SMO).…”

Section: Evaluation Of Proposed Bffsbr Feature Selection Methodsmentioning

confidence: 99%

“…Feature selection [43] is a process of selecting a subset of the original features, yet produce similar or almost similar analytical results. It tries to select a minimum set of features such that the probability distribution of different classes given the values for those features is as close as possible to the original distribution given the values of all features.…”

Section: Step-wise Floating Forward Selection and Backward Removalmentioning

confidence: 99%

A rough set theory and deep learning based predictive system for gender recognition using audio speech

Yasmin

Das

Nayak

et al. 2021

Preprint

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Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

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“…Enter the era of powerful computing, and it saw the massive upsurge in the field of Deep Learning approaches, which efficiently tackled unstructured higher dimensional data with ease and provided an astounding accuracy whenever applied to any field of study. As such a plethora of Deep Learning work has summoned the door of dealing with the high dimension of microarray data and derived output classification based on that, but still they bear that ideology of transformation to some lower order variant via previous dimensionality reduction algorithms [3][4][5][6][7], Heatmaps [8], Statistical inferences [9], etc. and doesn't maximize the full utility of neural networks which are some excellent feature extractors.…”

Section: Introductionmentioning

confidence: 99%

Feature Mining and Classification of Microarray Data Using Modified ResNet-SVM Architecture

Bej¹,

Roy²,

Suchinta³

et al. 2021

Computational Intelligence in Pattern Recognition

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Microarray data have been one of the torch-bearing approaches when ushered for prediction and diagnosis of deadly diseases. But, much of its potential has been overshadowed by its structural orientation of low sample and high dimension which have provided the dilemma for researchers working in this field of study. In recent times, many approaches have been adopted to tackle this dilemma through feature extraction and thereafter classification that have stapled very concrete results. But, the methodologies lacked direct implementation and required the transformation of the data into a relevant intermediate matrix, before being mobilized for feature extraction and classification. In this paper, an effective hybrid alternative has been proposed which utilizes a modified single-dimensional (1D) Residual Network (ResNet) for lucid feature extraction directly from the gathered datasets and a Support Vector Machine (SVM) classifier for diseases classification. The proposed methodology is tested on three datasets (namely Colon cancer, Leukemia, and Lung cancer) and derived a staggering classification accuracy.

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

Cited by 12 publications

References 31 publications

A Graph Based Clustering Approach for Relation Extraction From Crime Data

A Graph Based Clustering Approach for Relation Extraction From Crime Data

A rough set theory and deep learning based predictive system for gender recognition using audio speech

Feature Mining and Classification of Microarray Data Using Modified ResNet-SVM Architecture

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