Practical feature subset selection for machine learning

Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithm

Feature subset selection: a correlation based filter approach

Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees

Melody based tune retrieval over the World Wide Web

In this paper we describe the steps taken to develop a Web-based version of an existing stand-alone, single-user digital library application for melodical searching of a collection of music. For the three key components: input, searching, and output, we assess the suitability of various Web-based strategies that deal with the now distributed software architecture and explain the decisions we made. The resulting melody indexing service, known as MELDEX, has been in operation for one year, and the feed-back we have received has been favorable

TESTING FOR DIFFERENCES IN CONSUMER ACCEPTANCE OF IDENTICALLY APPEARING POTATO VARIETIES

Like many other vegetables, potatoes are marketed by type (russet, round white, red), rather than by variety (Burbak, Katahdin, Pontiac). Although varieties of the same type have similar outward appearances, they are also known to have different internal and cooking characteristics. There has been considerable controversy over the need for variety identification promotion in the potato industry. A consumer response study that distinguished between user satisfaction with different potato varieties was viewed as a step toward resolving this issue.Consumer/Household Economics,

The effect of left hemisphere brain tumours and their resection on speech production and visual processing

Functional reorganization may explain why. despite a large tumour in eloquent cortex. the patient has no neurological impairment. The aims of the present study were to 1. Investigate the effect of tumour growth on neural circuits for speech production and visual processing and 2. Determine the effect of tumour removal on speech production and visual processing. Three patients with large, left-hemisphere brain tumours had pre- surgery and post-surgery functional neuroimaging (fMRI) and language testing (CAT). In addition, these patients underwent surgery for tumour resection. Pre-operative fMRI demonstrated functional reorganization in the patients. All three patients showed regions of overactivation and underactivation in local and remote regions relative to tumour location. Of particular interest, two patients showed increased activity in the right hemisphere homologue of their left parietal tumour whereas one patient illustrated a decreased activation in the right hemisphere homologue region to her left postcentral tumour. A comparison of pre and post-surgery fMRI results demonstrated that functional reorganization primarily occurs prior to surgery although some changes in activation occur after surgery. This study provides evidence that the right hemisphere homologue region is differentially activated (over and under) across patients. Furthermore, our study suggests that the effect of brain tumour growth is more prominent than tumour resection