Data description and classification are important tasks in supervised learning. In this study, three supervised learning methods such as k-nearest neighbour (k-NN), support vector data description (SVDD) and support vector machine (SVM) are considered because they do not suffer from the problem...

The primary intension of any investor in the stock market is to catch the market trends at an early stage and accordingly transact (buy or sell) at the right time. Though stock market data is convertible into some form of multiple time series, it is difficult to process, analyse and mine...

This paper discussed possible application of soft computing methods in uncertainty management of the HR component in steel industry during automation period. It explained the importance of HR, the concept of uncertainty management and risk. Risk assessment map of Rourkela Steel Plant is...

We have demonstrated the equivalence of two formulations for data processing, which usually are studied separately and independently. Habitually, for time series analysis, like signals processed in communications, electronics, control sciences, etc., mathematical tools based on autocorrelations,...

The competitiveness of the global market and availability of multiple outsourcing options have greatly increased the complexity of making 'good' outsourcing decisions. Improving the decision-making process when faced with a complex decision situation requires a systematic decision-making...

We use the theory of generalised additive models to develop a non-parametric cost function for the Canadian cable television industry through the 1990s. We offer that statistical testing for substitutability/complementarity of inputs is important for regulators and policymakers since some forms...

We extend our previous works of deriving linguistic summaries of time series using a fuzzy logic approach. To obtain a more advanced evaluation of summaries, we perform a multi-criteria analysis of summaries by assuming as a unifying quality criterion Yager's measure of informativeness of the...

The Malmquist index, based on data envelopment analysis (DEA) models, is the prominent index for measuring the relative productivity change of decision-making units (DMUs) in multiple time periods. This study presents new insight into the Malmquist index for measuring the productivity change of...

When it is difficult to get learning data during the training time, we have to classify objects by having extremely small information about their feature. It is assumed in the paper that only some average or mean value of every feature and the lower and upper bounds of a set of its values are...

Association rule mining is applied to large databases to identify product associations. In the resulting large number of rules, interestingness is difficult to determine. Researchers have defined various measures of 'interestingness' such as support, confidence, lift and gain. Support is the...