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AbstractÐIn this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.

A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models.

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures.

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

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Context:Polycystic ovarian syndrome (PCOS), the most common endocrinopathy of women in the reproductive age group seems to be adversely affected by associated thyroid dysfunction. Both pose independent risks of ovarian failure and pregnancy related complications.Aims:The present study from Eastern India is, therefore, aimed to investigate the prevalence and etiology of different thyroid disorders in PCOS subjects.Settings and Design:Cross-sectional hospital based survey-single centre observational case-control study.Materials and Methods:This prospective single-center study recruited 106 female patients with hypertrichosis and menstrual abnormality among which 80 patients were defined as having PCOS according to the revised 2003 Rotterdam criteria and comprised the study population. Another 80 age-matched female subjects were studied as the control population. Thyroid function and morphology were evaluated by measurement of serum thyroid stimulating hormone (TSH), free thyroxine levels (free T3 and free T4), anti-thyroperoxidase antibody (anti-TPO Ab), clinical examination and ultrasound (USG) of thyroid gland.Statistical Analysis Used:It was done by Student's t-test and Chi-square test using appropriate software (SPSS version 19).Results:This case-control study revealed statistically significant higher prevalence of autoimmune thyroiditis, detected in 18 patients (22.5% vs. 1.25% of control) as evidenced by raised anti-TPO antibody levels (means 28.037 ± 9.138 and 25.72 ± 8.27 respectively; P = 0.035). PCOS patients were found to have higher mean TSH level than that of the control group (4.547 ± 2.66 and 2.67 ± 3.11 respectively; P value < 0.05). There was high prevalence of goiter among PCOS patients (27.5% vs. 7.5% of control, P value > 0.001). On thyroid USG a significantly higher percentage of PCOS patients (12.5%; controls 2.5%) had hypoechoic USG pattern also compatible with the diagnosis of autoimmune thyroiditis.Conclusions:High prevalence of thyroid disorders in PCOS patients thus points towards the importance of early correction of hypothyroidism in the management of infertility associated with PCOS.