Analysis of a feature-deselective neuroevolution classifier (FD-NEAT) in a computer-aided lung nodule detection system for CT images

Tan, Maxine; Deklerck, Rudi; Jansen, Bart; Cornelis, Jan

doi:10.1145/2330784.2330869

Cited by 6 publications

(2 citation statements)

References 30 publications

(48 reference statements)

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“…Feature deselective-NEAT (FD-NEAT) follows an evolutionary feature selection strategy similar to FS-NEAT, but biases the subset search to favor larger selected feature subsets [48,49]. The FD-NEAT algorithm begins with a fully connected network just as NEAT does, and then removes connections via a remove connection mutation operation during evolution.…”

Section: Fd-neatmentioning

confidence: 99%

Predictive feature selection for genetic policy search

Loscalzo

Wright

2014

Auton Agent Multi-Agent Syst

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Automatic learning of control policies is becoming increasingly important to allow autonomous agents to operate alongside, or in place of, humans in dangerous and fast-paced situations. Reinforcement learning (RL), including genetic policy search algorithms, comprise a promising technology area capable of learning such control policies. Unfortunately, RL techniques can take prohibitively long to learn a sufficiently good control policy in environments described by many sensors (features). We argue that in many cases only a subset of available features are needed to learn the task at hand, since others may represent irrelevant or redundant information. In this work, we propose a predictive feature selection framework that analyzes data obtained during execution of a genetic policy search algorithm to identify relevant features on-line. This serves to constrain the policy search space and reduces the time needed to locate a sufficiently good policy by embedding feature selection into the process of learning a control policy. We explore this framework through an instantiation called predictive feature selection embedded in neuroevolution of augmenting topology (NEAT), or PFS-NEAT. In an empirical study, we demonstrate that PFS-NEAT is capable of enabling NEAT to successfully find good control policies in two benchmark environments, and show that it can outperform three competing feature selection algorithms, FS-NEAT, FD-NEAT, and SAFS-NEAT, in several variants of these environments.

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Section: Fd-neatmentioning

confidence: 99%

Predictive feature selection for genetic policy search

Loscalzo

Wright

2014

Auton Agent Multi-Agent Syst

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“…29,30 NEAT's performance has been analyzed and proven in various and diverse domains. 28,[31][32][33][34][35] Recently, it was shown that Phased Searching with NEAT in a Time-Scaled Framework 26 and another variant of NEAT (feature-deselective NEAT or FD-NEAT) 36,37 performed well in a lung nodule detection scheme of CT images. The advantage of Phased Searching over conventional NEAT is that feature selection is enabled in Phased Searching, and it produces simpler networks than FD-NEAT and NEAT, which are faster to train and validate, and require less parameter (connection weight) tuning.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

Tan

2014

Cancer Inform

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In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.

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