| 1. |
- Boldt, Martin, et al.
(författare)
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Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data
- 2018
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 22:1, s. 167-190
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The present study aims to extend current research on how offenders’ modus operandi (MO) can be used in crime linkage, by investigating the possibility to automatically estimate offenders’ risk exposure and level of pre-crime preparation for residential burglaries. Such estimations can assist law enforcement agencies when linking crimes into series and thus provide a more comprehensive understanding of offenders and targets, based on the combined knowledge and evidence collected from different crime scenes. Methods: Two criminal profilers manually rated offenders’ risk exposure and level of pre-crime preparation for 50 burglaries each. In an experiment we then analyzed to what extent 16 machine-learning algorithms could generalize both offenders’ risk exposure and preparation scores from the criminal profilers’ ratings onto 15,598 residential burglaries. All included burglaries contain structured and feature-rich crime descriptions which learning algorithms can use to generalize offenders’ risk and preparation scores from.Results: Two models created by Naïve Bayes-based algorithms showed best performance with an AUC of 0.79 and 0.77 for estimating offenders' risk and preparation scores respectively. These algorithms were significantly better than most, but not all, algorithms. Both scores showed promising distinctiveness between linked series, as well as consistency for crimes within series compared to randomly sampled crimes.Conclusions: Estimating offenders' risk exposure and pre-crime preparation can complement traditional MO characteristics in the crime linkage process. The estimations are also indicative to function for cross-category crimes that otherwise lack comparable MO. Future work could focus on increasing the number of manually rated offenses as well as fine-tuning the Naïve Bayes algorithm to increase its estimation performance.
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| 2. |
- Dudas, Catarina, et al.
(författare)
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Post-analysis of multi-objective optimization solutions using decision trees
- 2015
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 19:2, s. 259-278
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Tidskriftsartikel (refereegranskat)abstract
- Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.
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| 3. |
- Flyckt, Jonatan, et al.
(författare)
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Explaining rifle shooting factors through multi-sensor body tracking
- 2023
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 27:2, s. 535-554
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Tidskriftsartikel (refereegranskat)abstract
- There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but have mostly focused on single aspects of postural control. This study has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. A data collection was performed with 13 human participants carrying out live rifle shooting scenarios while being recorded with multiple body tracking sensors. A pre-processing pipeline produced a novel skeleton sequence representation, which was used to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (80%). Shooting performance could be detected to an extent by separating participants using their strong and weak shooting hand. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this study have laid the groundwork for future research in the sports shooting domain.
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| 4. |
- García Martín, Eva, et al.
(författare)
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Energy modeling of Hoeffding tree ensembles
- 2021
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 25:1, s. 81-104
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Tidskriftsartikel (refereegranskat)abstract
- Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.
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| 5. |
- Haghighi, Pari Delir, et al.
(författare)
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Context-aware adaptive data stream mining
- 2009
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Ingår i: Intelligent Data Analysis. - 1088-467X .- 1571-4128. ; 13:3, s. 423-434
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Tidskriftsartikel (refereegranskat)abstract
- In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of context-awareness. This paper presents a general approach for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and situations. We perform intelligent and real-time analysis of data streams generated from sensors that is under-pinned using context-aware adaptation. A prototype of the proposed architecture is implemented and evaluated in the paper through a real-world scenario in the area of healthcare monitoring.
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| 6. |
- Johansson, Ulf, et al.
(författare)
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Obtaining accurate and comprehensible classifiers using oracle coaching
- 2012
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 16:2, s. 247-263
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Tidskriftsartikel (refereegranskat)abstract
- While ensemble classifiers often reach high levels of predictive performance, the resulting models are opaque and hence do not allow direct interpretation. When employing methods that do generate transparent models, predictive performance typically has to be sacrificed. This paper presents a method of improving predictive performance of transparent models in the very common situation where instances to be classified, i.e., the production data, are known at the time of model building. This approach, named oracle coaching, employs a strong classifier, called an oracle, to guide the generation of a weaker, but transparent model. This is accomplished by using the oracle to predict class labels for the production data, and then applying the weaker method on this data, possibly in conjunction with the original training set. Evaluation on 30 data sets from the UCI repository shows that oracle coaching significantly improves predictive performance, measured by both accuracy and area under ROC curve, compared to using training data only. This result is shown to be robust for a variety of methods for generating the oracles and transparent models. More specifically, random forests and bagged radial basis function networks are used as oracles, while J48 and JRip are used for generating transparent models. The evaluation further shows that significantly better results are obtained when using the oracle-classified production data together with the original training data, instead of using only oracle data. An analysis of the fidelity of the transparent models to the oracles shows that performance gains can be expected from increasing oracle performance rather than from increasing fidelity. Finally, it is shown that further performance gains can be achieved by adjusting the relative weights of training data and oracle data.
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| 7. |
- Karunaratne, Thashmee, 1968-, et al.
(författare)
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Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling
- 2013
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 17:2, s. 327-341
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Tidskriftsartikel (refereegranskat)abstract
- Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods.
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| 8. |
- Kotsifakos, Alexios, et al.
(författare)
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Query-sensitive Distance Measure Selection for Time Series Nearest Neighbor Classification
- 2016
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Ingår i: Intelligent Data Analysis. - 1088-467X .- 1571-4128. ; 20:1, s. 5-27
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Tidskriftsartikel (refereegranskat)abstract
- Many distance or similarity measures have been proposed for time series similarity search. However, none of these measures is guaranteed to be optimal when used for 1-Nearest Neighbor (NN) classification. In this paper we study the problem of selecting the most appropriate distance measure, given a pool of time series distance measures and a query, so as to perform NN classification of the query. We propose a framework for solving this problem, by identifying, given the query, the distance measure most likely to produce the correct classification result for that query. From this proposed framework, we derive three specific methods, that differ from each other in the way they estimate the probability that a distance measure correctly classifies a query object. In our experiments, our pool of measures consists of Dynamic TimeWarping (DTW), Move-Split-Merge (MSM), and Edit distance with Real Penalty (ERP). Based on experimental evaluation with 45 datasets, the best-performing of the three proposed methods provides the best results in terms of classification error rate, compared to the competitors, which include using the Cross Validation method for selecting the distance measure in each dataset, as well as using a single specific distance measure (DTW, MSM, or ERP) across all datasets.
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| 9. |
- Lindgren, Tony, et al.
(författare)
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Resolving rule conflicts with double induction
- 2004
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 8:5, s. 457-468
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Tidskriftsartikel (refereegranskat)abstract
- When applying an unordered set of classification rules, the rules may assign more than one class to a particular example. Previous methods of resolving such conflicts between rules include using the most frequent class of the examples covered by the conflicting rules (as done in CN2) and using naïve Bayes to calculate the most probable class. An alternative way of solving this problem is presented in this paper: by generating new rules from the examples covered by the conflicting rules. These newly induced rules are then used for classification. Experiments on a number of domains show that this method significantly outperforms both the CN2 approach and naïve Bayes.
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| 10. |
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