Multi-label Classification via Adaptive Resonance Theory-based Clustering

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning

FML-based Prediction Agent and Its Application to Game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.Comment: 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 201

Diagnostic Performance of 11C-choline PET/CT and FDG PET/CT in Prostate Cancer

We compared 11C-choline and FDG PET/CT scan findings for the staging and restaging of prostate cancer. Twenty Japanese prostate cancer patients underwent 11C-choline and FDG PET/CT before (n=5) or after (n=15) treatment. Using a five-point scale, we compared these scanning modalities regarding patient- and lesion-based diagnostic performance for local recurrence, untreated primary tumor, and lymph node and bony metastases. Of the 20 patients, documented local lesions, and node and bony metastases were present in 11 (55.0%), 9 (45.0%), and 13 (65.0%), respectively. The patient-based sensitivity/specificity/accuracy/area under the receiver-operating-characteristic curve (AUC) values for 11C-choline-PET/CT for diagnosing local lesions were 90.9% /100%/ 95.0% / 1.0, whereas those for FDG-PET/CT were 45.5% /100%/ 75.0% / 0.773. Those for 11C-choline-PET/CT for node metastasis were 88.9% /100%/ 95.0% / 0.944, and those for FDG-PET/CT were 44.4%/100%/75.0%/0.722. Those for 11C-choline-PET/CT for bone metastasis were 84.6%/100%/90.0%/0.951, and those for FDG-PET/CT were 76.9% /100%/ 85.0% / 0.962. The AUCs for local lesion and node metastasis differed significantly (p=0.0039, p=0.011, respectively). The lesion-based detection rates of 11C-choline compared to FDG PET/CT for local lesion, and node and bone metastases were 91.7% vs. 41.7%, 92.0% vs. 32.0%, and 94.8% vs. 83.0% (p=0.041, p=0.0030, p<0.0001), respectively. 11C-choline-PET/CT is more useful for the staging and restaging of prostate cancer than FDG-PET/CT in Japanese men

Evaluation of Treatment Response in Prostate Cancer and Renal Cell Carcinoma Patients Using 11C-choline PET/CT Findings

We investigated the effectiveness of 11C-choline-positron emission tomography/computed tomography (PET/CT) for evaluating treatment response in patients with prostate cancer or renal cell carcinoma. We performed 34 11C-choline PET/CT scans before/after a combined total of 17 courses of treatment in 6 patients with prostate cancer and 2 with renal cell carcinoma. The 17 treatments including hormonal therapy, radiotherapy, chemotherapy, radium-223, molecular target therapy, radiofrequency ablation, transcatheter arterial embolization, and cancer immunotherapy yielded 1 (5.9%) complete metabolic response (CMR), 3 (17.6%) partial metabolic responses (PMRs), 2 (11.8%) stable metabolic diseases (SMDs), and 11 (64.7%) progressive metabolic diseases (PMDs). Target lesions were observed in bone (n=14), lymph nodes (n=5), lung (n=2), prostate (n=2), and pleura (n=1), with CMR in 4, PMR in 10, SMD in 8 and PMD in 2 lesions. SUVmax values of the target lesions before and after treatment were 7.87±2.67 and 5.29±3.98, respectively, for a mean reduction of −35.4±43.6%. The response for the 8 prostate cancer-treatment courses was PMD, which correlated well with changes in serum prostatic specific antigen (PSA) (7 of 8 cases showed increased PSA). 11C-choline-PET/CT may be an effective tool for detecting viable residual tumors and evaluating treatment response in prostate cancer and renal cell carcinoma patients

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns