Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.

Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.

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BackgroundThe Montreal Cognitive Assessment (MoCA) is known to have discriminative power for patients with Mild Cognitive Impairment (MCI). Recently Cognitive Reserve (CR) has been introduced as a factor that compensates cognitive decline. We aimed to assess whether the MoCA reflects CR. Furthermore, we assessed whether there were any differences in the efficacy between the MoCA and the Mini-Mental State Examination (MMSE) in reflecting CR.MethodsMoCA, MMSE, and the Cognitive Reserve Index questionnaire (CRIq) were administered to 221 healthy participants. Normative data and associated factors of the MoCA were identified. Correlation and regression analyses of the MoCA, MMSE and CRIq scores were performed, and the MoCA score was compared with the MMSE score to evaluate the degree to which the MoCA reflected CR.ResultsThe MoCA reflected total CRIq score (CRI; B = 0.076, P < 0.001), CRI-Education (B = 0.066, P <  0.001), and CRI-Working activity (B = 0.025, P = 0.042), while MMSE reflected total CRI (B = 0.044, P <  0.001) and CRI-Education (B = 0.049, P <  0.001) only. The MoCA differed from the MMSE in the reflection of total CRI (Z = 2.30).ConclusionIn this study, we show that the MoCA score reflects CR more sensitively than the MMSE score. Therefore, we suggest that MoCA can be used to assess CR and early cognitive decline.Electronic supplementary materialThe online version of this article (10.1186/s12877-018-0951-8) contains supplementary material, which is available to authorized users.

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