BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260721T105739Z UID:9BDA476A-75EB-4E8E-ADCA-0ABCC39CD98C DTSTART;TZID=Asia/Kolkata:20260710T190000 DTEND;TZID=Asia/Kolkata:20260710T200000 DESCRIPTION:Feature selection is a critical preprocessing step in machine l earning that aims to identify the most relevant attributes from high‑dim ensional datasets. By eliminating redundant and irrelevant features\, it r educes computational complexity\, mitigates overfitting\, and enhances mod el generalization. Effective feature selection improves learning accuracy while maintaining interpretability\, making models more transparent and ef ficient. Techniques are broadly categorized into filter methods\, which re ly on statistical measures\; wrapper methods\, which evaluate subsets usin g predictive models\; and embedded methods\, which integrate selection dur ing model training. Recent advances also explore hybrid approaches and evo lutionary algorithms to balance accuracy and efficiency. Applications span diverse domains such as bioinformatics\, text mining\, and healthcare ana lytics\, where large feature spaces are common. Overall\, feature selectio n not only accelerates training but also strengthens decision‑making\, e nsuring robust and scalable machine learning solutions for complex real‑ world problems.\n\nSpeaker(s): Dr. Satya Verma\, \n\nVirtual: https://even ts.vtools.ieee.org/m/565756 LOCATION:Virtual: https://events.vtools.ieee.org/m/565756 ORGANIZER:amiya87@gmail.com SEQUENCE:50 SUMMARY:Feature Selection in Machine Learning URL;VALUE=URI:https://events.vtools.ieee.org/m/565756 X-ALT-DESC:Description: <br /><p>Feature selection is a critical preprocess ing step in machine learning that aims to identify the most relevant attri butes from high‑dimensional datasets. By eliminating redundant and irrel evant features\, it reduces computational complexity\, mitigates overfitti ng\, and enhances model generalization. Effective feature selection improv es learning accuracy while maintaining interpretability\, making models mo re transparent and efficient. Techniques are broadly categorized into <str ong>filter methods</strong>\, which rely on statistical measures\; <strong >wrapper methods</strong>\, which evaluate subsets using predictive models \; and <strong>embedded methods</strong>\, which integrate selection durin g model training. Recent advances also explore hybrid approaches and evolu tionary algorithms to balance accuracy and efficiency. Applications span d iverse domains such as bioinformatics\, text mining\, and healthcare analy tics\, where large feature spaces are common. Overall\, feature selection not only accelerates training but also strengthens decision‑making\, ens uring robust and scalable machine learning solutions for complex real‑wo rld problems.</p> END:VEVENT END:VCALENDAR